{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "9438d717",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--- 正在配置项目文件路径 ---\n",
      "\n",
      "--- 阶段 1: 正在加载所有预处理好的源数据 ---\n",
      "✅ 所有源数据加载成功。\n",
      "\n",
      "--- 阶段 2: 正在进行坐标转换与空间处理 ---\n",
      "✅ 空间处理完成。\n",
      "\n",
      "--- 阶段 3: 正在格式化模型输入数据 ---\n",
      "✅ 数据格式化完成。\n",
      "\n",
      "--- 阶段 4: 正在生成路网缓存并打包最终输入文件 ---\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2025-10-13 21:49:43,141 - INFO - 构建分层路网...\n",
      "2025-10-13 21:49:43,141 - INFO - 正在将路网几何体添加到图中...\n",
      "处理路网几何体: 100%|██████████| 18355/18355 [00:00<00:00, 59164.87it/s]\n",
      "2025-10-13 21:49:43,455 - INFO - 几何体处理完毕。总共处理了 18355 条线。\n",
      "2025-10-13 21:49:43,455 - INFO - 成功向图中添加了 29047 条边。\n",
      "2025-10-13 21:49:43,456 - INFO - 构建空间索引 (KDTree 和 Grid)...\n",
      "2025-10-13 21:49:43,481 - INFO - 构建多层次索引...\n",
      "2025-10-13 21:49:43,489 - INFO - 识别出 2615 个Hub节点。\n",
      "2025-10-13 21:49:43,490 - INFO - 预计算Hub节点间的捷径...\n",
      "计算捷径: 100%|██████████| 2615/2615 [00:11<00:00, 230.20it/s]\n",
      "2025-10-13 21:49:54,851 - INFO - 预计算所有节点的Hub接入距离 (这可能需要几分钟)...\n",
      "计算接入距离: 100%|██████████| 22717/22717 [00:55<00:00, 407.96it/s] \n",
      "2025-10-13 21:50:50,539 - INFO - Hub接入距离预计算完成。\n",
      "2025-10-13 21:50:50,541 - INFO - 分层路网构建完成: 22717 节点, 29043 边, 耗时: 67.4s\n",
      "2025-10-13 21:50:51,420 - INFO - 路网缓存已保存: road_cache.pkl\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  - ✅ 路网缓存已成功生成: 'road_cache.pkl'\n",
      "  - ✅ 所有输入数据已成功打包到: 'pso_input_data.pkl'\n",
      "\n",
      "🎉 [成功] 所有数据准备流程完成！\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_1376/3158420674.py:93: DeprecationWarning: The 'unary_union' attribute is deprecated, use the 'union_all()' method instead.\n",
      "  \"boundary_polygon_3857\": boundary_gdf_3857.unary_union,\n"
     ]
    }
   ],
   "source": [
    "# =========================================================================\n",
    "# === 主数据准备脚本 (最终版 v6 - 无GCJ-02转换) ========================\n",
    "# =========================================================================\n",
    "# 描述:\n",
    "#   本脚本假设所有输入的CSV和Shapefile中的地理坐标都【已经是】\n",
    "#   标准的WGS-84格式。它不再进行任何gcj2wgs坐标校正。\n",
    "# =========================================================================\n",
    "\n",
    "import pandas as pd\n",
    "import geopandas as gpd\n",
    "from shapely.geometry import Point\n",
    "import pickle\n",
    "import os\n",
    "import traceback\n",
    "import warnings\n",
    "\n",
    "# --- 0. 环境设置 ---\n",
    "warnings.simplefilter(action='ignore', category=FutureWarning)\n",
    "# 🔥 [核心修改] 不再导入和定义 gcj2wgs 函数\n",
    "\n",
    "# --- 1. 统一管理文件路径 ---\n",
    "print(\"--- 正在配置项目文件路径 ---\")\n",
    "# 🔥 [核心] 现在所有输入文件都应该是WGS-84标准的\n",
    "BOUNDARY_SHAPEFILE = \"仓山区最终完整边界.shp\" \n",
    "POI_CSV = \"poi仓山区.csv\" # <--- 必须使用已校正的CSV\n",
    "POPULATION_SHAPEFILE = \"population.shp\"\n",
    "ROAD_SHAPEFILE = \"仓山区修复后路网.shp\" \n",
    "ruixing_csv = \"福州市仓山区瑞幸咖啡坐标.csv\"\n",
    "\n",
    "ROAD_CACHE_PATH = \"road_cache.pkl\"         \n",
    "PSO_INPUT_DATA_PATH = \"pso_input_data.pkl\"\n",
    "\n",
    "# =========================================================================\n",
    "# === 主处理流程 ==========================================================\n",
    "# =========================================================================\n",
    "try:\n",
    "    print(\"\\n--- 阶段 1: 正在加载所有预处理好的源数据 ---\")\n",
    "    \n",
    "    # a. 加载边界\n",
    "    boundary_gdf_wgs84 = gpd.read_file(BOUNDARY_SHAPEFILE)\n",
    "    if boundary_gdf_wgs84.crs is None:\n",
    "        boundary_gdf_wgs84.set_crs(\"EPSG:4326\", inplace=True)\n",
    "    \n",
    "    # b. 加载POI\n",
    "    poi_df = pd.read_csv(POI_CSV, encoding='utf-8')\n",
    "    \n",
    "    # c. 加载人口\n",
    "    population_gdf = gpd.read_file(POPULATION_SHAPEFILE)\n",
    "    \n",
    "    # d. 加载路网\n",
    "    road_gdf = gpd.read_file(ROAD_SHAPEFILE)\n",
    "\n",
    "    print(\"✅ 所有源数据加载成功。\")\n",
    "\n",
    "    # --- 阶段 2: 坐标转换与空间处理 ---\n",
    "    print(\"\\n--- 阶段 2: 正在进行坐标转换与空间处理 ---\")\n",
    "    \n",
    "    boundary_gdf_3857 = boundary_gdf_wgs84.to_crs(\"EPSG:3857\")\n",
    "    population_gdf_3857 = population_gdf.to_crs(\"EPSG:3857\")\n",
    "    \n",
    "    # 🔥 [核心修改] 不再进行gcj2wgs转换，直接使用CSV中的坐标\n",
    "    poi_gdf_wgs84 = gpd.GeoDataFrame(\n",
    "        poi_df,\n",
    "        geometry=gpd.points_from_xy(poi_df.geolocation_lng, poi_df.geolocation_lat),\n",
    "        crs=\"EPSG:4326\"\n",
    "    )\n",
    "    poi_in_boundary = gpd.sjoin(poi_gdf_wgs84, boundary_gdf_wgs84, how=\"inner\", predicate='within')\n",
    "    poi_gdf_3857 = poi_in_boundary.to_crs(\"EPSG:3857\")\n",
    "    \n",
    "    print(\"✅ 空间处理完成。\")\n",
    "\n",
    "    # --- 阶段 3: 格式化为模型所需的最终数据结构 ---\n",
    "    print(\"\\n--- 阶段 3: 正在格式化模型输入数据 ---\")\n",
    "    poi_data_3857 = [\n",
    "        ((row.geometry.x, row.geometry.y), row['category'], row['weight'])\n",
    "        for _, row in poi_gdf_3857.iterrows()\n",
    "    ]\n",
    "    population_points_3857 = [\n",
    "        ((p.x, p.y), value) \n",
    "        for p, value in zip(population_gdf_3857.geometry, population_gdf_3857['population'])\n",
    "        if value > 0\n",
    "    ]\n",
    "    print(\"✅ 数据格式化完成。\")\n",
    "\n",
    "    # --- 阶段 4: 生成路网缓存并打包所有数据 ---\n",
    "    print(\"\\n--- 阶段 4: 正在生成路网缓存并打包最终输入文件 ---\")\n",
    "    from pso_core_gpu import HierarchicalRoadNetwork\n",
    "    if os.path.exists(ROAD_CACHE_PATH): os.remove(ROAD_CACHE_PATH)\n",
    "    _ = HierarchicalRoadNetwork(road_gdf, cache_file=ROAD_CACHE_PATH)\n",
    "    print(f\"  - ✅ 路网缓存已成功生成: '{ROAD_CACHE_PATH}'\")\n",
    "    \n",
    "    pso_input_data = {\n",
    "        \"boundary_polygon_3857\": boundary_gdf_3857.unary_union,\n",
    "        \"poi_data_3857\": poi_data_3857,\n",
    "        \"population_points_3857\": population_points_3857,\n",
    "        \"road_network_cache\": ROAD_CACHE_PATH \n",
    "    }\n",
    "    with open(PSO_INPUT_DATA_PATH, \"wb\") as f:\n",
    "        pickle.dump(pso_input_data, f)\n",
    "    print(f\"  - ✅ 所有输入数据已成功打包到: '{PSO_INPUT_DATA_PATH}'\")\n",
    "\n",
    "    print(f\"\\n🎉 [成功] 所有数据准备流程完成！\")\n",
    "\n",
    "except Exception as e:\n",
    "    print(f\"\\n❌ [严重错误] 在数据准备阶段发生错误: {e}\")\n",
    "    traceback.print_exc()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "b611b0de",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--- 开始生成坐标系诊断地图 ---\n",
      "\n",
      "[步骤 1/4] 正在加载原始文件...\n",
      "✅ 所有原始数据加载成功。\n",
      "\n",
      "[步骤 2/4] 正在创建诊断地图...\n",
      "\n",
      "[步骤 3/4] 正在处理并绘制所有点位...\n",
      "  - 已绘制原始(漂移的)点。\n",
      "  - 已绘制转换后(校正的)点。\n",
      "\n",
      "[步骤 4/4] 正在绘制边界并保存地图...\n",
      "  - 警告：边界文件缺少CRS，将假定其为WGS-84。\n",
      "\n",
      "🎉 [成功] 诊断地图已生成: 仓山区优化结果地图.html\n",
      "   请用浏览器打开此文件进行分析！\n"
     ]
    }
   ],
   "source": [
    "# =========================================================================\n",
    "# === 坐标系诊断脚本 (v3 - 终极稳健版) =====================================\n",
    "# =========================================================================\n",
    "# 描述:\n",
    "#   本脚本是用于坐标系问题诊断的最终版本。它会：\n",
    "#   1. 健壮地加载所有原始数据文件。\n",
    "#   2. 使用`eviltransform`进行坐标转换。\n",
    "#   3. 在一张可交互地图上同时绘制转换前（漂移的）和转换后（校正的）\n",
    "#      的点位，以及边界，以便进行最终的视觉验证。\n",
    "#\n",
    "# 使用方法:\n",
    "#   1. 确保在能成功运行`eviltransform`的Python环境中执行。\n",
    "#   2. 检查下面的文件路径。\n",
    "#   3. 运行脚本，然后打开生成的 .html 文件进行分析。\n",
    "# =========================================================================\n",
    "\n",
    "import pandas as pd\n",
    "import geopandas as gpd\n",
    "from shapely.geometry import Point\n",
    "import folium\n",
    "import warnings\n",
    "import sys\n",
    "\n",
    "# --- 0. 环境设置 ---\n",
    "warnings.simplefilter(action='ignore', category=FutureWarning)\n",
    "try:\n",
    "    from coord_convert.transform import gcj2wgs\n",
    "except ImportError:\n",
    "    print(\"❌ 致命错误：找不到 'eviltransform' 库。\")\n",
    "    print(\"   请在您能成功运行此库的Python环境中执行此脚本。\")\n",
    "    print(\"   安装命令: pip install eviltransform\")\n",
    "    sys.exit(1)\n",
    "\n",
    "# --- 1. 文件路径定义 ---\n",
    "BOUNDARY_SHP_PATH = \"仓山区最终完整边界.shp\" # 您的边界文件\n",
    "POI_CSV_PATH = \"poi仓山区.csv\"        # 您手动分类好的POI\n",
    "STORES_CSV_PATH = \"福州市仓山区瑞幸咖啡坐标.csv\"\n",
    "OUTPUT_MAP_HTML = \"仓山区优化结果地图.html\"\n",
    "\n",
    "# --- 2. 健壮的CSV加载函数 ---\n",
    "def load_csv_robustly(filepath: str) -> pd.DataFrame:\n",
    "    \"\"\"尝试用UTF-8和GBK两种编码加载CSV文件。\"\"\"\n",
    "    try:\n",
    "        return pd.read_csv(filepath, encoding='utf-8')\n",
    "    except UnicodeDecodeError:\n",
    "        print(f\"  - 提示: 文件 '{filepath}' 使用UTF-8读取失败，正在尝试GBK...\")\n",
    "        return pd.read_csv(filepath, encoding='gbk')\n",
    "\n",
    "# =========================================================================\n",
    "# === 3. 主执行流程 =======================================================\n",
    "# =========================================================================\n",
    "def main():\n",
    "    print(\"--- 开始生成坐标系诊断地图 ---\")\n",
    "\n",
    "    # --- 步骤 1: 加载所有原始数据 ---\n",
    "    print(\"\\n[步骤 1/4] 正在加载原始文件...\")\n",
    "    try:\n",
    "        boundary_gdf_raw = gpd.read_file(BOUNDARY_SHP_PATH)\n",
    "        poi_df_raw = load_csv_robustly(POI_CSV_PATH)\n",
    "        stores_df_raw = load_csv_robustly(STORES_CSV_PATH)\n",
    "        print(\"✅ 所有原始数据加载成功。\")\n",
    "    except Exception as e:\n",
    "        print(f\"❌ 加载文件时出错: {e}\")\n",
    "        return\n",
    "\n",
    "    # --- 步骤 2: 创建地图和图层组 ---\n",
    "    print(\"\\n[步骤 2/4] 正在创建诊断地图...\")\n",
    "    map_center = [26.07, 119.30] # 福州市中心大致坐标\n",
    "    m = folium.Map(location=map_center, zoom_start=12, tiles=\"OpenStreetMap\")\n",
    "\n",
    "    # 创建四个独立的图层组，用于存放不同类型的数据点\n",
    "    group_poi_raw = folium.FeatureGroup(name=\"原始POI (红色 - GCJ02)\")\n",
    "    group_stores_raw = folium.FeatureGroup(name=\"原始瑞幸门店 (绿色 - GCJ02)\")\n",
    "    group_poi_corrected = folium.FeatureGroup(name=\"转换后POI (蓝色 - WGS84)\", show=False) # 默认不显示\n",
    "    group_stores_corrected = folium.FeatureGroup(name=\"转换后瑞幸门店 (黑色 - WGS84)\", show=False) # 默认不显示\n",
    "\n",
    "    # --- 步骤 3: 绘制所有点位 ---\n",
    "    print(\"\\n[步骤 3/4] 正在处理并绘制所有点位...\")\n",
    "    \n",
    "    # a. 绘制原始点 (GCJ-02)\n",
    "    for _, row in poi_df_raw.iterrows():\n",
    "        folium.CircleMarker(\n",
    "            location=[row['geolocation_lat'], row['geolocation_lng']],\n",
    "            radius=4, color='red', fill_opacity=0.7\n",
    "        ).add_to(group_poi_raw)\n",
    "    for _, row in stores_df_raw.iterrows():\n",
    "        folium.CircleMarker(\n",
    "            location=[row['geolocation_lat'], row['geolocation_lng']],\n",
    "            radius=4, color='green', fill_opacity=0.7\n",
    "        ).add_to(group_stores_raw)\n",
    "    print(\"  - 已绘制原始(漂移的)点。\")\n",
    "\n",
    "    # b. 执行坐标转换\n",
    "    corrected_poi_coords = [gcj2wgs(lon, lat) for lon, lat in zip(poi_df_raw.geolocation_lng, poi_df_raw.geolocation_lat)]\n",
    "    corrected_stores_coords = [gcj2wgs(lon, lat) for lon, lat in zip(stores_df_raw.geolocation_lng, stores_df_raw.geolocation_lat)]\n",
    "\n",
    "    # c. 绘制转换后的点 (WGS-84)\n",
    "    for lon, lat in corrected_poi_coords:\n",
    "        folium.CircleMarker(\n",
    "            location=[lat, lon],\n",
    "            radius=3, color='blue', fill=True, fill_color='blue', fill_opacity=1.0\n",
    "        ).add_to(group_poi_corrected)\n",
    "    for lon, lat in corrected_stores_coords:\n",
    "        folium.CircleMarker(\n",
    "            location=[lat, lon],\n",
    "            radius=3, color='black', fill=True, fill_color='black', fill_opacity=1.0\n",
    "        ).add_to(group_stores_corrected)\n",
    "    print(\"  - 已绘制转换后(校正的)点。\")\n",
    "    \n",
    "    # --- 步骤 4: 绘制边界并保存地图 ---\n",
    "    print(\"\\n[步骤 4/4] 正在绘制边界并保存地图...\")\n",
    "    \n",
    "    if boundary_gdf_raw.crs is None:\n",
    "        print(\"  - 警告：边界文件缺少CRS，将假定其为WGS-84。\")\n",
    "        boundary_gdf_raw.set_crs(\"EPSG:4326\", inplace=True)\n",
    "    boundary_gdf_wgs84 = boundary_gdf_raw.to_crs(\"EPSG:4326\")\n",
    "    \n",
    "    # 使用半透明样式，以便能看到下方的底图\n",
    "    folium.GeoJson(\n",
    "        boundary_gdf_wgs84, \n",
    "        name=\"研究区域边界\",\n",
    "        style_function=lambda x: {'color': '#333333', 'weight': 2, 'fillColor': 'purple', 'fillOpacity': 0.2}\n",
    "    ).add_to(m)\n",
    "\n",
    "    # 将所有图层组添加到地图\n",
    "    group_poi_raw.add_to(m)\n",
    "    group_stores_raw.add_to(m)\n",
    "    group_poi_corrected.add_to(m)\n",
    "    group_stores_corrected.add_to(m)\n",
    "    \n",
    "    # 添加图层控制器，以便可以切换显示\n",
    "    folium.LayerControl().add_to(m)\n",
    "    \n",
    "    # 保存地图\n",
    "    m.save(OUTPUT_MAP_HTML)\n",
    "    \n",
    "    print(f\"\\n🎉 [成功] 诊断地图已生成: {OUTPUT_MAP_HTML}\")\n",
    "    print(\"   请用浏览器打开此文件进行分析！\")\n",
    "\n",
    "if __name__ == \"__main__\":\n",
    "    main()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "d4ff7789",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "\n",
    "import geopandas as gpd\n",
    "import pandas as pd\n",
    "from shapely.geometry import Point\n",
    "import folium\n",
    "from folium.plugins import HeatMap\n",
    "import os\n",
    "\n",
    "def create_scenario_map(\n",
    "        scenario_name: str, \n",
    "        optimal_locations_3857: list, \n",
    "        boundary_gdf_wgs84: gpd.GeoDataFrame,\n",
    "        poi_gdf_wgs84: gpd.GeoDataFrame,\n",
    "        population_gdf_3857: gpd.GeoDataFrame,\n",
    "        road_network_gdf_3857: gpd.GeoDataFrame,\n",
    "        actual_locations_gdf: gpd.GeoDataFrame,\n",
    "        output_folder: str = \"final_maps\"\n",
    "    ):\n",
    "    \"\"\"\n",
    "    一个功能强大的函数，用于生成包含所有分析图层的综合地图。\n",
    "    所有输入的GeoDataFrame都应是已经加载好的。\n",
    "    \"\"\"\n",
    "    print(f\"-> 正在为情景 '{scenario_name}' 生成地图...\")\n",
    "    \n",
    "    # --- a. 准备数据 ---\n",
    "    optimal_gdf_wgs84 = gpd.GeoDataFrame(\n",
    "        geometry=[Point(loc) for loc in optimal_locations_3857], crs=\"EPSG:3857\"\n",
    "    ).to_crs(\"EPSG:4326\")\n",
    "    \n",
    "    pop_gdf_wgs84 = population_gdf_3857.to_crs(\"EPSG:4326\")\n",
    "    heatmap_data = [\n",
    "        [p.y, p.x, v] for p, v in \n",
    "        zip(pop_gdf_wgs84.geometry, population_gdf_3857['population'])\n",
    "    ]\n",
    "\n",
    "    # --- b. 创建地图 ---\n",
    "    map_center = [boundary_gdf_wgs84.unary_union.centroid.y, boundary_gdf_wgs84.unary_union.centroid.x]\n",
    "    m = folium.Map(location=map_center, zoom_start=12, tiles=\"OpenStreetMap\")\n",
    "\n",
    "    # --- c. 添加图层 ---\n",
    "    HeatMap(heatmap_data, name=\"人口热力图\", radius=15).add_to(m)\n",
    "    \n",
    "    folium.GeoJson(\n",
    "        road_network_gdf_3857.to_crs(\"EPSG:4326\"),\n",
    "        name=\"路网\", style_function=lambda x: {'color': 'gray', 'weight': 1}, show=False\n",
    "    ).add_to(m)\n",
    "    \n",
    "    poi_group = folium.FeatureGroup(name=\"POI点\", show=False)\n",
    "    for _, poi in poi_gdf_wgs84.iterrows():\n",
    "        folium.CircleMarker(\n",
    "            location=[poi.geometry.y, poi.geometry.x], radius=3, color='blue',\n",
    "            fill=True, fill_color='blue', popup=poi.get('name', 'POI')\n",
    "        ).add_to(poi_group)\n",
    "    poi_group.add_to(m)\n",
    "\n",
    "    actual_group = folium.FeatureGroup(name=\"真实瑞幸门店\")\n",
    "    # 确保坐标系正确\n",
    "    if actual_locations_gdf.crs.to_epsg() != 4326:\n",
    "        actual_locations_gdf = actual_locations_gdf.to_crs(\"EPSG:4326\")\n",
    "    for _, store in actual_locations_gdf.iterrows():\n",
    "        folium.Marker(\n",
    "            location=[store.geometry.y, store.geometry.x], \n",
    "            icon=folium.Icon(color='green', icon='coffee', prefix='fa'),\n",
    "            popup=store.get('name', '门店')\n",
    "        ).add_to(actual_group)\n",
    "    actual_group.add_to(m)\n",
    "\n",
    "    optimized_group = folium.FeatureGroup(name=\"模型推荐位置\")\n",
    "    for idx, point in enumerate(optimal_gdf_wgs84.geometry):\n",
    "        folium.Marker(\n",
    "            location=[point.y, point.x], popup=f\"最优站点 {idx+1}\", \n",
    "            icon=folium.Icon(color='red', icon='star', prefix='fa')\n",
    "        ).add_to(optimized_group)\n",
    "    optimized_group.add_to(m)\n",
    "\n",
    "    folium.GeoJson(\n",
    "        boundary_gdf_wgs84, name=\"研究区域边界\",\n",
    "        style_function=lambda x: {'color': 'black', 'weight': 3, 'fillOpacity': 0.1}\n",
    "    ).add_to(m)\n",
    "    \n",
    "    # --- d. 保存地图 ---\n",
    "    bounds = boundary_gdf_wgs84.total_bounds\n",
    "    m.fit_bounds([[bounds[1], bounds[0]], [bounds[3], bounds[2]]])\n",
    "    folium.LayerControl().add_to(m)\n",
    "    os.makedirs(output_folder, exist_ok=True)\n",
    "    map_path = os.path.join(output_folder, f\"{scenario_name}_map.html\")\n",
    "    m.save(map_path)\n",
    "    print(f\"-> 地图已成功保存至: {map_path}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "f6a778ca",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "================================================================================\n",
      "🚀 即将开始在外部Python进程中执行GPU加速的PSO优化...\n",
      "💻 脚本的所有输出（包括日志和错误）都将实时显示在下方。\n",
      "================================================================================\n",
      "\n",
      "--- GPU加速优化脚本已启动 ---\n",
      "正在加载输入数据...\n",
      "✅ 输入数据加载并解包成功。\n",
      "2025-09-28 00:39:57,351 - INFO - 强制从缓存 'road_cache.pkl' 加载路网...\n",
      "2025-09-28 00:39:57,547 - INFO - 路网缓存加载完成\n",
      "2025-09-28 00:39:57,547 - INFO - --- [GPU] Initializing GPUEvaluator with Population Density Enhancement ---\n",
      "/home/asunak/miniconda3/envs/rapids-24.04/lib/python3.10/site-packages/cugraph/structure/symmetrize.py:93: FutureWarning: Multi is deprecated and the removal of multi edges will no longer be supported from 'symmetrize'. Multi edges will be removed upon creation of graph instance.\n",
      "warnings.warn(\n",
      "2025-09-28 00:39:58,174 - INFO - ✅ [GPU] cuGraph graph created on GPU.\n",
      "2025-09-28 00:39:58,182 - INFO - ✅ [GPU] 11646 population points loaded to GPU.\n",
      "2025-09-28 00:39:58,182 - INFO -   - Pre-calculating population density factor for each POI...\n",
      "2025-09-28 00:39:58,470 - INFO - ✅ [GPU] 163 POI points loaded and enhanced with population data.\n",
      "2025-09-28 00:39:58,470 - INFO - --- [GPU] GPUEvaluator initialization finished in 0.92s ---\n",
      "\n",
      "==================== 开始执行GPU加速的高精度优化 ====================\n",
      "2025-09-28 00:39:58,470 - INFO - Initializing OptimizedMultiSitePSO with GPU support...\n",
      "2025-09-28 00:39:58,470 - INFO - 正在生成有效点缓存...\n",
      "2025-09-28 00:39:58,526 - INFO - 有效点缓存生成完毕: 2000 个点\n",
      "缓存点样本: [(13276133.306753533, 3001937.3846471175), (13282376.89234424, 3005716.9307835596), (13272526.7077803, 3011232.170339338), (13285460.811413126, 2997221.815551032), (13284302.028023483, 3005679.7542220172)]\n",
      "2025-09-28 00:39:58,526 - INFO - PSO优化器初始化完成 - 站点数: 50, 粒子数: 20\n",
      "2025-09-28 00:39:58,526 - INFO - === 开始PSO优化 (评估模式: precise) ===\n",
      "2025-09-28 00:39:58,526 - INFO - 正在初始化粒子群...\n",
      "2025-09-28 00:39:58,526 - INFO - 随机生成初始粒子群...\n",
      "2025-09-28 00:39:58,527 - INFO - 正在执行初始评估...\n",
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      "2025-09-28 00:44:06,184 - INFO - 初始化完成 - 初始最优值: -184.2795\n",
      "2025-09-28 00:44:06,184 - INFO - 正在开始主优化循环...\n",
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      "2025-09-29 05:10:27,848 - INFO - Iteration 398/400 | Best Score: -2348.2191 | w: 0.21, c1: 1.40, c2: 2.60\n",
      "2025-09-29 05:10:27,848 - INFO - Iteration 398/400 | Best Score: -2348.2191\n",
      "\n",
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      "迭代 399/400:  65%|██████▌   | 13/20 [02:09<01:09,  9.99s/it]\n",
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      "迭代 399/400:  85%|████████▌ | 17/20 [02:48<00:29,  9.88s/it]\n",
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      "迭代 399/400: 100%|██████████| 20/20 [03:17<00:00,  9.88s/it]\n",
      "2025-09-29 05:13:45,548 - INFO - Iteration 399/400 | Best Score: -2377.1059 | w: 0.20, c1: 1.40, c2: 2.60\n",
      "2025-09-29 05:13:45,548 - INFO - Iteration 399/400 | Best Score: -2377.1059\n",
      "\n",
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      "迭代 400/400:  65%|██████▌   | 13/20 [02:08<01:08,  9.81s/it]\n",
      "迭代 400/400:  70%|███████   | 14/20 [02:19<00:59,  9.92s/it]\n",
      "迭代 400/400:  75%|███████▌  | 15/20 [02:29<00:49,  9.98s/it]\n",
      "迭代 400/400:  80%|████████  | 16/20 [02:39<00:40, 10.11s/it]\n",
      "迭代 400/400:  85%|████████▌ | 17/20 [02:49<00:30, 10.08s/it]\n",
      "迭代 400/400:  90%|█████████ | 18/20 [02:59<00:20, 10.15s/it]\n",
      "迭代 400/400:  95%|█████████▌| 19/20 [03:10<00:10, 10.18s/it]\n",
      "迭代 400/400: 100%|██████████| 20/20 [03:20<00:00, 10.13s/it]\n",
      "迭代 400/400: 100%|██████████| 20/20 [03:20<00:00, 10.01s/it]\n",
      "2025-09-29 05:17:05,740 - INFO - Iteration 400/400 | Best Score: -2377.1059 | w: 0.20, c1: 2.00, c2: 2.00\n",
      "2025-09-29 05:17:05,740 - INFO - Iteration 400/400 | Best Score: -2377.1059\n",
      "2025-09-29 05:17:05,749 - INFO - PSO优化完成。总耗时: 103027.22s，最终最优值: -2377.1059\n",
      "\n",
      "优化成功！正在进行最终方案分析...\n",
      "2025-09-29 05:17:15,629 - INFO - 使用CPU路网模型进行详细的解决方案分析...\n",
      "✅ 优化结果已成功保存到 'pso_results.pkl'\n",
      "\n",
      "--- GPU加速优化脚本执行完毕 ---\n",
      "\n",
      "================================================================================\n",
      "🎉 [成功] 外部脚本执行完毕。总耗时: 1717.81 分钟。\n",
      "   优化结果已保存到 'pso_results.pkl' 文件中。\n",
      "================================================================================\n"
     ]
    }
   ],
   "source": [
    "import subprocess\n",
    "import time\n",
    "\n",
    "print(\"=\"*80)\n",
    "print(\"🚀 即将开始在外部Python进程中执行GPU加速的PSO优化...\")\n",
    "print(\"💻 脚本的所有输出（包括日志和错误）都将实时显示在下方。\")\n",
    "print(\"=\"*80)\n",
    "\n",
    "# 定义要执行的命令\n",
    "command = [\"python\", \"-u\", \"run_pso_gpu.py\"] # -u 参数确保实时输出\n",
    "start_time = time.time()\n",
    "return_code = -1 \n",
    "\n",
    "try:\n",
    "    # 启动子进程，并将标准输出和标准错误合并\n",
    "    process = subprocess.Popen(\n",
    "        command,\n",
    "        stdout=subprocess.PIPE,\n",
    "        stderr=subprocess.STDOUT,\n",
    "        text=True,\n",
    "        encoding='utf-8',\n",
    "        errors='replace' # 防止因编码问题中断\n",
    "    )\n",
    "\n",
    "    # 实时读取并打印子进程的输出\n",
    "    while True:\n",
    "        output = process.stdout.readline()\n",
    "        if output == '' and process.poll() is not None:\n",
    "            break\n",
    "        if output:\n",
    "            print(output.strip())\n",
    "\n",
    "    return_code = process.poll()\n",
    "\n",
    "finally:\n",
    "    end_time = time.time()\n",
    "    total_minutes = (end_time - start_time) / 60\n",
    "    print(\"\\n\" + \"=\"*80)\n",
    "    if return_code == 0:\n",
    "        print(f\"🎉 [成功] 外部脚本执行完毕。总耗时: {total_minutes:.2f} 分钟。\")\n",
    "        print(f\"   优化结果已保存到 'pso_results.pkl' 文件中。\")\n",
    "    else:\n",
    "        print(f\"❌ [错误] 外部脚本执行失败！返回码: {return_code}\")\n",
    "        print(\"   请仔细阅读上面的输出日志，找到导致脚本崩溃的错误信息。\")\n",
    "    print(\"=\"*80)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "2314c625",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "--- 开始加载和分析优化结果 ---\n",
      "✅ 结果文件 'pso_results.pkl' 加载成功。\n",
      "\n",
      "======================================== ✨ 最终方案分析报告 ✨ ========================================\n",
      "\n",
      "--- 优化摘要 ---\n",
      "  - 最终找到 50 个最优站点位置。\n",
      "  - 模型最终得分 (越低越好): -2377.1059\n",
      "\n",
      "--- 关键业务指标 ---\n",
      "  - 📈 实际人口覆盖率: 0.00%\n",
      "  - 🎯 POI服务率: 0.00%\n",
      "  - 📏 站点平均间距: 7496 米\n",
      "  - ⚠️ 站点最小间距: 172 米\n",
      "\n",
      "--- 成本构成 (0-1, 越低越好) ---\n",
      "  - POI便利性成本 (加权):   2896.3824\n",
      "  - 人口覆盖成本 (加权):     300.0000\n",
      "  - 站点间距成本 (加权):     0.0000\n",
      "\n",
      "--- 最优站点坐标 (WGS84 经纬度) ---\n",
      "  - 站点 01: (经度 119.354834, 纬度 26.017582)\n",
      "  - 站点 02: (经度 119.276700, 纬度 26.034441)\n",
      "  - 站点 03: (经度 119.287112, 纬度 26.031439)\n",
      "  - 站点 04: (经度 119.330000, 纬度 26.027434)\n",
      "  - 站点 05: (经度 119.350554, 纬度 25.989664)\n",
      "  - 站点 06: (经度 119.365019, 纬度 26.003371)\n",
      "  - 站点 07: (经度 119.317162, 纬度 26.025368)\n",
      "  - 站点 08: (经度 119.288237, 纬度 26.037712)\n",
      "  - 站点 09: (经度 119.330327, 纬度 26.030678)\n",
      "  - 站点 10: (经度 119.344604, 纬度 26.010841)\n",
      "  - 站点 11: (经度 119.330571, 纬度 26.003597)\n",
      "  - 站点 12: (经度 119.344281, 纬度 25.987566)\n",
      "  - 站点 13: (经度 119.344853, 纬度 25.994484)\n",
      "  - 站点 14: (经度 119.343409, 纬度 25.990208)\n",
      "  - 站点 15: (经度 119.329253, 纬度 26.031060)\n",
      "  - 站点 16: (经度 119.313702, 纬度 25.997678)\n",
      "  - 站点 17: (经度 119.310834, 纬度 26.034090)\n",
      "  - 站点 18: (经度 119.313638, 纬度 26.037332)\n",
      "  - 站点 19: (经度 119.344513, 纬度 26.021107)\n",
      "  - 站点 20: (经度 119.328429, 纬度 26.030108)\n",
      "  - 站点 21: (经度 119.307818, 纬度 25.999467)\n",
      "  - 站点 22: (经度 119.306205, 纬度 26.029968)\n",
      "  - 站点 23: (经度 119.306041, 纬度 26.023669)\n",
      "  - 站点 24: (经度 119.329054, 纬度 26.029130)\n",
      "  - 站点 25: (经度 119.330261, 纬度 26.026737)\n",
      "  - 站点 26: (经度 119.286391, 纬度 26.018279)\n",
      "  - 站点 27: (经度 119.310945, 纬度 26.033170)\n",
      "  - 站点 28: (经度 119.286676, 纬度 26.021335)\n",
      "  - 站点 29: (经度 119.324925, 纬度 26.014512)\n",
      "  - 站点 30: (经度 119.272053, 纬度 26.037920)\n",
      "  - 站点 31: (经度 119.333654, 纬度 25.991537)\n",
      "  - 站点 32: (经度 119.313438, 纬度 26.014338)\n",
      "  - 站点 33: (经度 119.329990, 纬度 26.027229)\n",
      "  - 站点 34: (经度 119.308491, 纬度 26.015044)\n",
      "  - 站点 35: (经度 119.309122, 纬度 26.034150)\n",
      "  - 站点 36: (经度 119.305908, 纬度 26.010409)\n",
      "  - 站点 37: (经度 119.288802, 纬度 26.017770)\n",
      "  - 站点 38: (经度 119.295141, 纬度 26.028143)\n",
      "  - 站点 39: (经度 119.298106, 纬度 26.021714)\n",
      "  - 站点 40: (经度 119.373498, 纬度 26.005590)\n",
      "  - 站点 41: (经度 119.345659, 纬度 26.029162)\n",
      "  - 站点 42: (经度 119.348324, 纬度 25.993480)\n",
      "  - 站点 43: (经度 119.325168, 纬度 26.016054)\n",
      "  - 站点 44: (经度 119.276813, 纬度 26.019370)\n",
      "  - 站点 45: (经度 119.346810, 纬度 25.992458)\n",
      "  - 站点 46: (经度 119.329575, 纬度 26.020124)\n",
      "  - 站点 47: (经度 119.289704, 纬度 26.004737)\n",
      "  - 站点 48: (经度 119.325651, 纬度 26.002264)\n",
      "  - 站点 49: (经度 119.279062, 纬度 26.022633)\n",
      "  - 站点 50: (经度 119.311177, 纬度 26.045030)\n",
      "====================================================================================================\n",
      "\n",
      "正在生成收敛曲线图...\n",
      "✅ 成功加载本地中文字体: SourceHanSansSC-Regular.otf\n"
     ]
    },
    {
     "data": {
      "image/png": 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Vr/vss89i06ZNWLVqFdq0aWMcnzFjBlatWoXo6Ghceuml6Nu3L5YsWYLFixcbOxUAFYtgVrWGAhERWQcnBYiIiC7Q119/bTxbXh8dO3bEW2+9hUcffRSPPvoovvnmGzz66KO48cYbERoaamxPeP5ic9VxnXf+tobnO3cRvuqsXbsWzZo1Q9++fY2FE2tjs9mq/Gm8y6FDhzB58mTjJ9T//Oc/8cQTT3j02lXRdd3493PXUJgwYQI++OADzJ07F+vWrUNQUJDbc/bn6tChAy699FJ88sknuOWWW3DZZZdh9uzZAIBvvvkGAQEBbnd8dOrUCVdccQXmz5+P7du3G4+KVGfbtm3Ytm1bpeNVLab43XffVTpW1VoSH3zwAaZPn44nn3wSAQEB+Ne//oWdO3ciISEBc+fORUhIiDEx9Pvvv2PJkiXIzMw0Pj49PR2DBg1Cly5d8NNPP13QhAwREcmLjw8QEREJomPHjvjss88QHR2NwsJCbN68GQBwySWXICAgAAUFBdi7d2+Nr5Gfn2/8xLmqZ+frQtd1rFu3DldddRX8/Pwu6LWAikULX3/9dXTt2hWffvopAgICAAC9e/f2+DVcuxmcPHkSH374IW688Ua88cYbVZ7bp08fJCYmYsaMGVixYgXuvfdeBAYGVvvaN910E7777juUlpaiW7duyM3NxZEjR/DVV1/hhhtuqLSLxD333IOtW7dCVVW3n75X5cUXX4Su68Y/11xzDdq3b+92zLVrwtq1ayude7733nsP9913H4CKSZWEhAQ8+OCDWLNmDXx9fREdHe12p0hMTAyaN29u3Plx8uRJ3HrrrQCAjz/+mBMCREQWxkkBIiIigfj5+Rm7FeTm5gIAfH19cddddwEA3nzzzRo//v/+7/9QUlKCxMTEWu8UqM327dtx/PhxDBo06IJex+XFF1/Ek08+iaCgIHzwwQd4/PHHazx/9+7d2Lhxo/FrXdeNn7a3adMGo0aNwpo1a2pcg+CZZ57BsWPHAAAPP/xwjZ9vyJAhOH36NNatW4cuXbrA398fP/zwA7Zu3VrlHQbXXXcdAKBnz56IiYmp8bUbWlpaGgICAjBs2DBMmzYNP/74I06ePIndu3dXuQuCzWbD5ZdfjnXr1uHw4cO4+uqrsXv3bnzyySdNPnYiIhILJwWIiIiaUElJCcaNG4f09PQq36+qKnbt2gXAfSvDF154AQ6HAx9++KGxtd759u7di3fffReKomD69OkXPNY333wT/v7+uPPOOy/4tQBg6tSpmD17Nvbv349Ro0bVuubAzp07cdVVVxnbJq5fv974venbty/+/e9/Izs725gwqUpycjKAim+K09LSavx8l112Ga699loEBASgW7duyM/Px2+//Yb27dtjwIABbndpqKqKcePGAQB+++03t8mLpvDss88iLy8Py5Ytw9NPP42BAwfWeBcEUDHpcezYMfTs2RMZGRlYvnw5Bg8e3EQjJiIiUXFSgIiIqAlNnz4dc+fORc+ePbFgwQKcOXPGeF9eXh7Gjh2L/fv3o2/fvm6LFLZv3x6PP/44SktLcd1112Hx4sXGrgQlJSVYunQp+vfvj7y8PNx55511uiW/Kvv378enn36KBx54oNbdAF599VW8/vrrtb6mj48Pxo0bV+k2/OoUFBQA+GOhxauvvhqPPfYYkpOTsWHDBowePbrG1/r666/x6KOPIjY2Fi1atMDIkSOxZ8+eGse3evVqXHXVVVAUBSkpKVi4cCFOnTqFsLAwt0cEpkyZglWrVuH+++9HeHg4xowZ4/GaCw0hJCQEvr6+xq+LiorwzTff4MEHH8QzzzxT6fzTp0/j4MGDACoWVVy/fr1xp8O5azIQEZH1cFKAiIioCT355JO46667UFhYiPvuuw9hYWHo3r07unTpgrZt2+Lf//43oqOj8emnn8Jut7t97CuvvILx48ejuLgY99xzD1q2bIk//elPaNmyJW666Sbk5+fj1ltvxYIFCy54nC+99BJ8fX3x1FNP1XpuRkYG5s6dW+/PVd03pa67KSIjIwFUfDP79ttvo3v37rW+5qpVq3DnnXeidevWWLVqFT7//HOcOnUKN9xwg/HN8bnKy8vx66+/4p133sEtt9yC0NBQXHbZZSgoKEB0dDQeeughvPHGG9A0Da+99hreeOMN3HrrrZg9ezbmzZuH33//HQ899JCx5kFjczqd2LRpE15++WUMGDAAoaGhGD58OL766iu3nQo0TcOCBQvQuXNnzJ49Gw6HA06nEx07djTOue6665CVldUk4yYiIgHpREREVC/XXHONDkD/+uuv6/yxmzdv1u+//369c+fOerNmzfSIiAh9wIAB+muvvaYXFxfX+LHr1q3T77jjDr1du3a6n5+fHhkZqd944436N998U6/rGDdunH7uXwlmzJihA9DfeOMNt/MKCgp0APqECRPcjg8bNkxPSEio8rUXLVqkA9AXLVpU6X2uzzN//vxK7ysvL9d79Oiht2jRQnc6nTWO/9lnn9UB6BkZGXpRUZH+0EMP6Yqi6IGBgfq2bduM8+bMmaMD0IODg/WPPvrI7TUSExN1ADoAPTo6Wh87dqz+6aef6seOHTPO2bdvn56QkKAD0Pv376+fOXPGeN/YsWN1APqwYcP0wsJC4/jx48eN172Qfzp16uQ23kGDBhnv69mzp/7ss8/qW7Zs0TVN03Vd1w8ePKi/9NJLekxMjA5Aj4uL05cvX65/+eWXOgD9L3/5i15eXq7n5OToAPRNmzbV+HtMRETmxS0JiYiI6mnNmjX1/ti+ffuib9++9frYq6++GldffXW9P3dN5s2bh8mTJ2PKlCmYPHmy2/sCAwPh6+uLb775Bg6HAz4+PnA6nVi9ejUefPDBOn8u10/8H3zwQbz22mvw8fnjryXHjh1DXl4eRowY4XabfE1OnjyJgQMHIj09HT169MDHH3+Mbt26Ge9/4IEH0KxZMzz44IP429/+hvz8fGPxwb///e+49dZbkZiYiNjY2EqvffDgQVx66aUoLS3FM888gxdeeMFtXLNnz4bNZsPcuXNx3333GdstuvTs2RM9e/b0+PfmXCtWrKh0bPTo0Rg5ciRuuOEGtG/fvtL5w4YNg6Zp6N69O/71r3/h73//u3HnyR133IFPPvkEe/bsQbNmzRAYGFjvsRERkfw4KUBEREQAKm6h//HHH/HII4/g1VdfrfR+RVHw3HPP4a233sKMGTOMY927d8cjjzxS5883ePBgPPbYY1iyZAlSU1PdHiNo0aIFEhMTMWfOHI9fr2XLlrj99tuhaRpeeeWVKrdRvPvuu3HZZZdh6tSpGDNmjHH8jjvuqPG1O3bsiOnTp+Pyyy+vcqtHu92OOXPmoGvXrhg5cmSl9w8fPhwvvPCCx9dyrhEjRlS6vf9vf/tbtecPHToU06ZNwxVXXFHl5NGiRYsQFRWFDz/8EGVlZXjrrbfQrFmzeo2NiIjkp+g6V5chIiIiIiIisiIuNEhERERERERkUZwUICIiIiIiIrIoTgoQERERERERWRQnBYiIiIiIiIgsirsPNAFN03DkyBEEBgZCURRvD4eIiIiIiIhMTtd1FBUVoW3btrDZqr8fgJMCTeDIkSOIiory9jCIiIiIiIjIYrKystC+fftq389JgSYQGBgIoCJGUFCQl0dTPVVVsXv3bnTr1g12u93bw6FqsJP42EgO7CQHdhIfG8mBneTATuKTqVFhYSGioqKM70erw0mBJuB6ZCAoKEj4SYEWLVogKChI+D/gVsZO4mMjObCTHNhJfGwkB3aSAzuJT8ZGtT3CzoUGyaAoCkJCQrjugeDYSXxsJAd2kgM7iY+N5MBOcmAn8ZmxkaLruu7tQZhdYWEhgoODUVBQIPSdAkRERERERGQOnn4fyjsFyKBpGjIzM6FpmreHQjVgJ/GxkRzYSQ7sJD42kgM7yYGdxGfGRlxTgAy6riM/Px/t2rXz9lCoBuwkPjaSAzvJgZ3Ex0ZyYCc5iNhJ13WUl5dDVVVvD0UIqqoiLy8PoaGhXl9TwG63w8fH54IfZeCkABEREREREVXidDpx9OhRnDlzxttDEYau67DZbDh48KAQ6wo0a9YMbdq0gZ+fX71fg5MCRERERERE5EbTNKSnp8Nut6Nt27bw8/MT4ptgb9N1HSUlJXA4HF79/dB1HU6nE8eOHUN6ejo6d+4Mm61+qwNwUoAMiqIgMjKSX+yCYyfxsZEc2EkO7CQ+NpIDO8lBpE5OpxOapiEqKgrNmjXz9nCEoes6fHx8GuS2/QsVEBAAX19fHDx4EE6nEw6Ho16vw0kBMthsNkRGRnp7GFQLdhIfG8mBneTATuJjIzmwkxxE7FTfnz6blaIo8PX19fYwDA3Rh4XJoKoqUlNTuYiI4NhJfGwkB3aSAzuJj43kwE5yYKfGl5qaWueP0XXd7d9LSkrcjsmOkwLkpqioyNtDIA+wk/jYSA7sJAd2Eh8byYGd5MBODWvq1KmYMmUKAGDfvn245JJLapx0+eijjzBx4kS3YytXrsTQoUONX7u2I9R1HR9//HGNr7dp0ybEx8dX+/7Tp0/jxx9/9OhaGgsnBYiIiIiIiMg01qxZgwMHDlQ6vnHjRvTo0aParQTz8vIwceJELFu2DHv27DGOr127tspv7FVVxfz58/HYY4/VaXybNm3C888/jz//+c/o2LEj3nvvPWRlZaFNmzZo27YtoqKijH8cDge+//77Or1+XXFNASIiIiIiIjKNf/3rXxg6dCg6d+7sdnzjxo3o1atXlR9TXl6O+++/H7fddhtuuukmJCYmYsmSJbjqqquwdu1aOJ1O3HbbbQAqJgO++OIL+Pj44IsvvkCPHj0waNAgDB8+3KPx6bqOHj16QNd1hIWF4cMPPzTel5ycjNDQUOPXiYmJdbz6uuOkABkURUFUVJTXV9GkmrGT+NhIDuwkB3YSHxvJgZ3kwE6NR9d1rFmzBllZWZg7d67b+5YtW4Y5c+bgyJEj+M9//gN/f3/897//xS233IIlS5bgl19+wddffw0fHx+MGTMG06ZNMz42JCQE8+fPR35+Pu69914sXry40udVVRW+vr4YMGAAfvjhB1x55ZUAgJMnTyIjI6PRr702nBQgg81mc5uVIjGxk/jYSA7sJAd2Eh8byYGd5CB6J13XUVxWv0UQf9ybi5/T83F5dAiu7dq6zh8f4Gu/oMmSH374AQcPHsRPP/2EhIQE6LqO8PBw/Pbbb9izZw8KCgrQpk0b9OzZ0/iYzZs346mnnkJ5eTkiIyMRHx+P4uJi3HPPPW5jGTJkCADgr3/9KxYsWOD2eTdt2oRx48YhOTnZOPbII49g6dKlOHnyJMrKyhAdHY0ZM2bAbrfjmmuugY/PH9+mp6enN/okEScFyKCqKg4cOIDOnTtX+5wNeR87iY+N5MBOcmAn8bGRHNhJDqJ3Ki5TEff8hT3fvjjpYL0+LuWlIWjmV79vXzVNw9SpUxEbG4sPPvgACQkJ2Lt3L1q1aoV27dqhXbt2GDRoUKVvvk+dOoV169bhkUcewcqVK+Hn54euXbuitLQUDoejym/WX3/9dYwcORIxMTHVjmfmzJmYOXMm+vfvj4iICHz55ZcAgBEjRtTr+i4UFxokNyUlJd4eAnmAncTHRnJgJzmwk/jYSA7sJAd2angZGRnIy8vDunXr8PXXX+P06dP44YcfMGDAAOOc999/H5mZmXjqqaewcuVKHD58GPfeey/27NmD8ePHY+XKlVi5ciUuu+wyYzvCKVOmICAgAHa73bhDoF27dhg5ciScTmeNY/rtt9+wc+dOrFixArNnz8aCBQsQHh5e5T+erlVQX7xTgIiIiIiIiGoV4GtHyktD6vxxP+7NxYT/bIddUaDqOt6789I6P0IQ4Fv/OydiYmKwcOFCNG/eHDfccAM+++wzzJs3DzNnzjTOKSkpwaeffoouXbpg06ZNSE1NRbdu3RAcHIzg4GAcP34cH3/8Md5//33jY6ZPn47p06fj7rvvNo7dddddWLZsGV5++WW8/PLL1Y7p3Xffxd/+9jecOHECc+fOxauvvopjx44BAD744AOsWLECS5Ysqfc11wUnBYiIiIiIiKhWiqLU6xb+Gy5pC38fOzan5aFfTCgS4yIaYXR/cDqdOHDgAF566SUsWrQId9xxB5o3bw4AeOihh3D99dejXbt2uOaaa4yPGTZsGO677z68/fbb+Prrr1FUVIT58+cb7x86dCiWLl2Kvn37ori4uMbP//bbbyMlJaXa9//222/Yu3cv7r33Xqxduxbvv/8+oqKiLuyiLwAfHyCDzWZDTEwMbDb+sRAZO4mPjeTATnJgJ/GxkRzYSQ5m7pQYF4Hnbohr9AmBkpISrFmzBvPmzUNBQYGx0r9Lp06dUFxcjKuvvtrteNeuXdGzZ090794dDz74IE6dOoWcnBwAwJkzZ7Bq1SoUFBQgPz8f/v7+NY4hMjIS/fr1Q2lpKXJyctwWDgSACRMmuN1FcMUVV8DhcKBNmzZo06YNHn30UXz99dfGr2fPnn0hvyW14p0CZPhhTy6SUvMQ4GdHsVOt9DYhtvFn9ah2iqIgKCjI28OgGrCRHNhJDuwkPjaSAzvJgZ0unMPhwCeffIKBAwfC19cXU6dONdZpOHPmDG644Qb8+c9/xpIlS3DVVVfhlltuAQCUlZXhb3/7G2bNmoXPP/8c+/btwz//+U989tlnGDNmDHr16oWIiAg8/PDD+Pjjj41FBvPz87F37160adPGbRyjRo3CN998g5CQEEyePNntfdOnT8eVV17ptlNBeHg4jh49CoCPD5CXLNmSiSlfJdd4zsJN6Zg3qg8nBrxMVVWkpKQgLi5OyFVpiY1kwU5yYCfxsZEc2EkO7NQwBg8eXOnYxo0bcd9996F379748MMPsXXrVvz5z3/Gzp078dRTT6FTp06IjY1FYmIi3nnnHfTt2xfJycm47LLLEBMTg48++gi6rmPgwIG4/fbbsXjxYuTk5GDAgAFISEjADz/8gJtvvhnjx49HQkICPv/8c2PiwOl04tixY8jPz0d0dHSluxe8jZMCBAD45WB+recoCrA5LY+TAgJQ1frtD0tNh43kwE5yYCfxsZEc2EkO7NTwNE3Dq6++ikmTJmHs2LEAgL59+2LLli149NFHkZOTg4yMjEqPBezZswcPP/ww7rvvPuPYypUr8dxzz6GsrAwffPABrr/+esyePRvl5eVYtGgRpk2bhl27duH06dOw2+3GXQoOhwMRERH4/fffK43v9OnT6NSpk/HrkpISOJ1OhIeHG8dSU1Mb7S4STgoQAGBg19b4YtvhGs/RdaBvdEgTjYiIiIiIiOjCvPDCC9W+r3Pnzli+fHm177/zzjsrHWvWrBleeeUVBAQEYMqUKcb6Dz4+PrjvvvuMCQRd1+F0OmG32yutKeBy77334t577wUAY+cBb+CkAAGoWBHU16bgu6370D4yAqWqDoevHSVlKmxKxaMDqgaEBda8qAYREREREZEV+Pn5Vfs+RVFqXZBQFJwUIMPgbpG4OrYlHA6H8fyLS95pJ77cdhif/3IIvTq08tIICahYlbZLly6mXJXWLNhIDuwkB3YSHxvJgZ3kwE5ycDgc3h5Cg+KkALmpbrbrtt5R+HLbYfz31yyomgabouCy6BD42BT8llXAnQmaWE2zkiQGNpIDO8mBncTHRnJgJzmwk/jO/wGq7DgFRQZN05CcnAxN0yq9r6ikDABQpur47JdD+GRrFiZ99hse/WQH/v1TOsYu/gWrUnKaesiWVFMnEgMbyYGd5MBO4mMjObCTHETspOu6t4cgnOLiYm8PwdAQfTgpQB7ZnJYPWzUTYroO2BUFm9PymnZQRERERETUKHx9fQEAZ86c8fJIqCauPq5e9cHHB8gjCbGhWLgpHTYAGgCbAmjnTEqpuo5+MaHeGh4RERERETUgu92Oli1bIjc3F0DFqvtmu22+PnRdR2lpKRRF8ervh67rOHPmDHJzc9GyZUvY7fZ6vxYnBcgjiXERmDeqDzan5Rm7EvSLCcW05XuQcfw07knoyDUFiIiIiIhMJDIyEgCMiQGq+Ga8rKwMvr6+QkyStGzZ0uhUX4rOh0QaXWFhIYKDg1FQUICgoCBvD6dauq5D0zTYbDaP/4C/tWo/Zq4+gOvjI/H+Xb0beYQE1K8TNS02kgM7yYGdxMdGcmAnOYjaSVVVlJWVeXsYQhCpka+vb413CHj6fSjvFCA3TqezTltsXNMlHDNXH8CGA8dRrmrwsXOZiqZQ107U9NhIDuwkB3YSHxvJgZ3kIGInu91+Qbenm4mu6ygpKalyG3dZcVKADJqmYd++fYiPj/f4i75H+5Zo1cwXJ86U4dFPdqBTWHMUO1UE+Nnr9DYhtmI9gqTUPLdjfCShsvp0oqbFRnJgJzmwk/jYSA7sJAd2Ep8ZG3FSgC6I3aagc+tA/JyRj++Sj9b7dRZuSnf7taJUHJs3qg8nBoiIiIiIiBoJ7/WmC9bMv+FnyLjNIRERERERUePjpAC5qc8tMHde3uGCPmd1T+Jwm8PqmeVWJTNjIzmwkxzYSXxsJAd2kgM7ic9sjbj7QBOQZfeBC7EqJcdtu8K6vnV98785LQ/LfjuCnKJSjOrXES8N7+7lKyMiIiIiIpIPdx+gOtN1HUVFRQgMDKzzSpqJcREN8ux/YlwEghy+ePuH/Th2qvSCX8+MLqQTNQ02kgM7yYGdxMdGcmAnObCT+MzYiI8PkEHTNKSlpUHTNK+O45ou4QCAjQeOo0z17lhEJEonqh4byYGd5MBO4mMjObCTHNhJfGZsxEkBEk58u2CENPdDUWk5tmee9PZwiIiIiIiITIuPD5BwbDYFV3UOwzc7juD5b3ahZ1RLY/0BH7uCARe35jaFREREREREDYCTAuTG4XB4ewgAgNDmfgCAvdlF2Jtd5Pa+jzZnYt6oPpaeGBClE1WPjeTATnJgJ/GxkRzYSQ7sJD6zNeLuA03ACrsPNLRnv07GR5szq3yfTQHG9I/GczfENfGoiIiIiIiI5ODp96FcU4AMmqYhLy9PiEUzBlzcGsAff0DPXddT02FsYWhFInWiqrGRHNhJDuwkPjaSAzvJgZ3EZ8ZGnBQgg67ryMrKggg3jyTGRWDeqD4Yc2U0Hhp4Ef5+ZTQuCm8OALitd3tLPzogUieqGhvJgZ3kwE7iYyM5sJMc2El8ZmzENQVIWIlxEW7f/Pv72PD72lQE+Nm9OCoiIiIiIiLz4J0CJI2okGYAgMz8M14eCRERERERkTlwUoDcBAYGensI1epwdlIgi5MCQneiCmwkB3aSAzuJj43kwE5yYCfxma0Rdx9oAtx9oGFk5p3B1W+sgb+PDXteGgqbTan9g4iIiIiIiCyIuw9QnWmahuzsbGFX0mzT0gGbApSWazh2qtTbw/Ea0TsRG8mCneTATuJjIzmwkxzYSXxmbMRJATLouo7s7GxhV9L0tdvQtmUAAGs/QiB6J2IjWbCTHNhJfGwkB3aSAzuJz4yNOClAUolqxcUGiYiIiIiIGgonBUgqfyw2WOzlkRAREREREcmPkwJkUBQFISEhUBRxF/CLCql4fMDKdwrI0Mnq2EgO7CQHdhIfG8mBneTATuIzYyMfbw+AxGGz2dChQwdvD6NGUa47BU5Yd1JAhk5Wx0ZyYCc5sJP42EgO7CQHdhKfGRvxTgEyaJqGzMxMoVfSNCYFLHyngAydrI6N5MBOcmAn8bGRHNhJDuwkPjM24p0CZNB1Hfn5+WjXrp23h1It10KDRwtK8NryPXCqGhy+dpSUqegXEwoA2JyWZxyr79umfS0NCbGhSIyL8Oj3QIZOVsdGcmAnObCT+NhIDuwkB3YSnxkbcVKApLI984Tx73PWp7m9b+GmjAb7PE39Wgs3pSOuTSDaBAegTNXga7dV+Ta2dQvouo4daccRsuNXlKt6teee+9bHrmBk7yiPJx6IiIiIiMgaOClAUtmclg8FgHl2Bf1DytEipBwtqvGc9QeO//GL7GN1ev3vd+dg3qg+nBggIiIiIiID1xQgg6IoiIyMFHolzYTYUOj44w+ua6S2c4Z8/vvq+rapX6upKKh4hIEanwxfS8ROsmAn8bGRHNhJDuwkPjM2UnRdN+MPXYVSWFiI4OBgFBQUICgoyNvDkd6qlJwqn8+XdU2BfdmF+GFPLmwANMC4E+L8tzYF0M5+tdZ27vlvXXinABERERGRNXj6fSgnBZqALJMCqqoiIyMDnTp1gt1u9/ZwLKW6iY6qJhg0TcPKHemICG2FUlWv9WMUBZi/IR0A8NNT16JtywAvX6358WtJDuwkB3YSHxvJgZ3kwE7ik6mRp9+Hck0BclNUVPMz7dQ4EuMiPP4JvqqqiCjPQXz8xR7/h2jbwRPYlnkSGw4cw18uM9e+qqLi15Ic2EkO7CQ+NpIDO8mBncRntkZcU4DIAq7qHA7gvIUKiYiIiIjI8ninAJEFXH1xGN5dfQCrdufg9RV7UVKmISG2Yq2DpNQ8BPjZUexUPX6bEBvKtQmIiIiIiEyAkwJkUBQFUVFRplpJ04zq0+lYUSkAwKlqeH9tKgBg4ab0eo9h4aZ0LlpYA34tyYGd5MBO4mMjObCTHNhJfGZsxMcHyGCz2RAaGgqbjX8sRFafTj+nn2jQLRBtCrc3rAm/luTATnJgJ/GxkRzYSQ7sJD4zNjLPldAFU1UVe/fuhaqq3h4K1aA+nRJiQ922JrzQCQJNh7HVIlXGryU5sJMc2El8bCQHdpIDO4nPjI34+AC5KSkp8fYQyAN17ZQYF4F5o/q4bXvo+qbek60QXW8zjp/G6r25uLh1Cz46UAt+LcmBneTATuJjIzmwkxzYSXxma8RJASKLqG7bw7p8c7/naCFW783F0YISaJoOm808z1IREREREVkRHx8gIo9d1LoF/HxsKCotR9aJM94eDhERERERXSBOCpDBZrMhJibGVItmmJE3O/nabegaGQgA2HW4sMk/vyz4tSQHdpIDO4mPjeTATnJgJ/GZsZF5roQumKIoCAoKMtX2Gmbk7U7d2gYBAHYfKfDK55eBtxuRZ9hJDuwkPjaSAzvJgZ3EZ8ZGnBQgg6qqSE5ONtVKmmbk7U7d2gYDAHYd4Z0C1fF2I/IMO8mBncTHRnJgJzmwk/jM2IiTAuTGTH+4zcybnYw7BQ4XQNf1Ws62Ln4tyYGd5MBO4mMjObCTHNhJfGZrxN0HiKhO/tQmCDYFyDvtxOe/HkKrZn5ISs1DgJ8dxU7V47cJsaHc1pCIiIiIyMs4KUBEdbLhwHFoZ28QeOK/O+v9Ogs3pWPeqD6cGCAiIiIi8iI+PkAGm82GLl26mGolTTPydqek1Dw0xLIqigJsTstrgFcSj7cbkWfYSQ7sJD42kgM7yYGdxGfGRua5EmoQfn5+3h4CecCbnRJiQ6EDsJ0zM1CfSQJdB/p0bNVQwxIOv5bkwE5yYCfxsZEc2EkO7CQ+szXi4wNk0DQNycnJiI+Ph91u9/ZwqBre7pQYF4F5o/pgc1oe+sWEAqj4ib/D146SMrXWt742BR8kHURxmYqi0vImH39T8HYj8gw7yYGdxMdGcmAnObCT+MzYiJMCRFRniXERbmsB1HVdgJAWfnh1+V68siwFGw4cQ2m5hj+1qdjVYM/RQvj72FBartX7bVWv5fCxISjAFwMubs11DIiIiIiIzuKkABE1uTbBAQCAwpJyLP3tKABg5e6cBnv9ml7ro82ZXOCQiIiIiOgsrilARE1ue+ZJtzUJmpJdUUy7wCERERERUV0puq7r3h6E2RUWFiI4OBgFBQUICgry9nCqpes6NE2DzWaDonjpOzaqlRk6rUrJwdjFv8AGQEPFooWubQ5dxxQAej3e1vRaLo19p4AZGlkBO8mBncTHRnJgJzmwk/hkauTp96F8fIDcOJ1OOBwObw+DaiF7p3MXK3QtQFifRQure1vVa63Zm4O042cwsle7Jnl0QPZGVsFOcmAn8bGRHNhJDuwkPrM14qQAGTRNw759+0y1kqYZmaXT+YsVnnu8IT+HS+tAf0z/394m2fHALI3Mjp3kwE7iYyM5sJMc2El8ZmzENQWIyBJ6dWwFANiWeRJ8aoqIiIiIqAInBYjIEuLbBcPHpuBYUSkOnSj29nCIiIiIiIRg2kmBTp06QVEUt3+eeuopt3MyMzNx4403onnz5ggLC8MjjzwCp9Ppdk5ycjIGDBiAgIAAtGvXDi+99JKpf8polltgzI6d6s7ha0e3thULrGzLPNHon4+N5MBOcmAn8bGRHNhJDuwkPrM1MvWaAi+99BLGjh1r/LpFixbGv6uqimHDhiE8PBwbN25EXl4e7rnnHui6jlmzZgGoWK0xMTERAwcOxNatW7F//36MHj0azZs3x6RJk5r8ehqb3W5HfHy8t4dBtWCn+uvVsRV+O1SAOetSsT/nFIqdKgL87DW+TYitWLQwKTWv1nPd39qx4sjvDfRaF/YaCbGhTbK4omz4tSQHdhIfG8mBneTATuIzYyNTTwoEBgYiMjKyyvetXLkSKSkpyMrKQtu2bQEAb775JkaPHo1p06YhKCgIH3/8MUpKSrBo0SL4+/uje/fu2L9/P9566y1MnDhR+C0o6krXdRQVFSEwMNB012Ym7FR/vraKm6P2HC3CnqNFHn3Mwk3pDfb5G+K16vMaCzelN/o2jDLi15Ic2El8bCQHdpIDO4nPjI1MPSnwz3/+Ey+//DKioqJw22234R//+Af8/PwAAElJSejevbsxIQAAQ4YMQWlpKX799VcMHDgQSUlJGDBgAPz9/d3OmTJlCjIyMhAdHV3l5y0tLUVpaanx68LCQgAVdyeoqgoAUBQFNpsNmqa5PY7gOu46r7bjrv0xqzoOVKyO6clxu90OVVWRmpqKbt26GbfE2O32SmOs7riI1+TaR7S2sct0TexU/2sqLHZ/PMgqbAqQlHoM13YJ++OYwJ1qO95Qf/YAVPpakv2a2EmOazJbJ1VVkZaWhu7du1f6C7Ks1+QaIzuJfU2uMbKT2NfkGqNZOrn+Ln7JJZcY4xH1mjx97N20kwKPPvooevXqhVatWuHnn3/GlClTkJ6ejvnz5wMAsrOzERHh/lOzVq1awc/PD9nZ2cY5nTp1cjvH9THZ2dnVTgpMnz4dU6dOrXR89+7dxiMMISEh6NChAw4dOoT8/HzjnMjISERGRiIjIwNFRX/8JDMqKgqhoaE4cOAASkpKjOMxMTEICgpCSkqK2x+CLl26wM/PD8nJyW5jiI+Ph9PpxL59+4xjrltgTp06hfz8fOzevRuKosDhcKBr1644ceIEsrKyjPMDAwMRGxuL3Nxc4/dK1GsqKipCWlqacdwM18RO9b+mmICKBQYVAJ78J/Lc8zz9GE9eq6lfQ9OBtj6n3ZqI3Mmlsf/sxcXFoayszPhaMsM1sZMc12S2Tq4up06dQkZGhimuiZ3kuCZ2kuOazNZJ13WcOFGxPpXo19SmTRt4QtElWjXvxRdfrPKb7XNt3boVffr0qXT8iy++wMiRI3H8+HGEhobi/vvvx8GDB/H999+7nefn54fFixfjjjvuwODBgxEdHY25c+ca7z98+DDat2+PpKQk9OvXr8oxVHWnQFRUFPLz8xEUVLHQmSgzXS52ux3l5eVITk7mT6AFvyZ2urBr+mFPLrak5yPAzwfFzjI4fOwoKVfh8LGjtFyD47zjfaNDoSgKtqTlwd/HZpxbUq7C4euD0jK18nEfO7KO5qBdZDicqm4cT4gNh65r2JyW5/55VR3+PgpKys59bTtKyysf7xcTCkWxISn1mNtrlJRrla5p44E87M89hVsubYcZI92ffRO9U03HG/In0Dt37uRPoAW/JoCdRL8mVVWRkpLCn2wKfk3sJMc1sZP416SqKnbv3i3FnQKnTp1Cy5YtUVBQYHwfWhWp7hSYMGEC7rjjjhrPOf8n+y6ub+B///13hIaGIjIyElu2bHE758SJEygrKzPuBoiMjHSbyQGA3NxcAKh0l8G5/P393R45cLHb7ZVWqnT9Iavq3KY+rigKAgICKo2zujHW9bi3rqmq47JfEzvV/5qGdG+DId09mzU91+BuVa9PUhVVVXHggILOnTtXeV2Du9X985/Pk/UBZq0+gDdX7Yetmt9fQNxOTXFcVdUqv5ZqGqPo1wSwU32Os9OFH3c4HKa7JoCd6nOcndipuuNmuqaAgAAA4l/T+RNL1ZFqUiAsLAxhYWG1n1iF7du3A/jjFoqEhARMmzYNR48eNY6tXLkS/v7+6N27t3HO008/DafTaaxFsHLlSrRt27bayQeZ2e12dO3a1dvDoFqwk/hEaRQTXvG4UuqxU14eiZhE6UQ1YyfxsZEc2EkO7CQ+MzaqekpCcklJSXj77bexY8cOpKen47PPPsMDDzyAm266CR06dAAADB48GHFxcbj77ruxfft2rF69GpMnT8bYsWONWyvuvPNO+Pv7Y/To0di1axe++uorvPrqq6bceQCouCUmLy+vyls3SRzsJD5RGsWENwcApB075fFCM1YiSieqGTuJj43kwE5yYCfxmbGRKScF/P398emnn+Kaa65BXFwcnn/+eYwdOxZLliwxzrHb7fjuu+/gcDjQv39/3H777Rg+fDhmzJhhnBMcHIxVq1bh0KFD6NOnD8aPH4+JEydi4sSJ3risRqfrOrKysvjNg+DYSXyiNIoOaw5FAQpLypF32po7L9RElE5UM3YSHxvJgZ3kwE7iM2MjqR4f8FSvXr2wefPmWs/r0KEDli1bVuM58fHxWL9+fUMNjYioyTh87WgbHIDDJ4uRduw0wlpUXuuEiIiIiKzNlHcKEBFRhXMfISAiIiIiOh8nBchNYGCgt4dAHmAn8YnSKPbsYoNpx097eSRiEqUT1YydxMdGcmAnObCT+MzWyJSPD1D92O12xMbGensYVAt2Ep9IjXinQPVE6kTVYyfxsZEc2EkO7CQ+MzbinQJk0DQN2dnZplpJ04zYSXwiNTLuFDjGOwXOJ1Inqh47iY+N5MBOcmAn8ZmxEScFyKDrOrKzs021kqYZsZP4RGrkulMg/fhp/PN/e/DS0hSsSsnx8qjEIFInqh47iY+N5MBOcmAn8ZmxEScFiIhMLPlQAQBABzB7XRoWbkrH2MW/cGKAiIiIiABwUoCIyNQ2p+VDOe+YogCb0/K8Mh4iIiIiEgsnBcigKApCQkKgKOd/C0EiYSfxidQoITYUOuA2MaDrQL+YUG8NSRgidaLqsZP42EgO7CQHdhKfGRspupkehhBUYWEhgoODUVBQgKCgIG8Ph4gsZlVKDjan5eFAbhHW7z+OS9oF49uHr/T2sIiIiIioEXn6fSjvFCCDpmnIzMw01UqaZsRO4hOtUWJcBJ67IQ73X1Wxfc4pZ7mXRyQG0TpR1dhJfGwkB3aSAzuJz4yNOClABl3XkZ+fb6qVNM2IncQnaqNOYc0AAFn5Z1Cumud/ZPUlaidyx07iYyM5sJMc2El8ZmzESQEiIotoExwAP7sNZaqOowUl3h4OEREREQmAkwJERBZhtynoEFpxt0D68dNeHg0RERERiYCTAmRQFAWRkZGmWknTjNhJfCI36hTaHACQkcdJAZE70R/YSXxsJAd2kgM7ic+MjXy8PQASh81mQ2RkpLeHQbVgJ/GJ3KjT2TsFMo6f8fJIvE/kTvQHdhIfG8mBneTATuIzYyPeKUAGVVWRmpoKVVW9PRSqATuJT+RGncJ4p4CLyJ3oD+wkPjaSAzvJgZ3EZ8ZGnBQgN0VFRd4eAnmAncQnaqNo16QA1xQAIG4ncsdO4mMjObCTHNhJfGZrxEkBIiILcd0pkHWC2xISEREREScFiIgspU2QA34+FdsSHjnJbQmJiIiIrI4LDZJBURRERUWZaiVNM2In8YncyGZTENrcD0cLSjB3fSpiwppj5+EC+PvYUFquVfnW38eOqy8OR2JchLeH36BE7kR/YCfxsZEc2EkO7CQ+MzZSdF3XvT0IsyssLERwcDAKCgoQFBTk7eEQkYWtSsnB2MW/1Otj543qY7qJASIiIiKz8vT7UD4+QAZVVbF3715TraRpRuwkPpEbJaXmwVbPie2Xl6Vgypc78fRXyViVktOwA/MCkTvRH9hJfGwkB3aSAzuJz4yNOClAbkpK+IyxDNhJfKI2SogNhaYD9nNueXP9j0Cp5q1LZv4ZLPk5C//Zkomxi38xxcSAqJ3IHTuJj43kwE5yYCfxma0R1xQgIrKQxLgIzBvVB5vT8tAvJhQAsDktDw5fO0rK1Crf/rg3B3uOum+9o5z9OD5OQERERCQ3TgoQEVlMYlyE2zfztX1j3zOqJcYu/gU2AK5NDHXAmFQgIiIiInlxUoAMNpsNMTExsNn4VInI2El8Zmt07t0FqqZh0U8HAVRMFsjMbJ3Mip3Ex0ZyYCc5sJP4zNiIuw80Ae4+QERmcuOsjUg+XIDXR16C2/tEeXs4RERERFQF7j5AdaaqKpKTk021kqYZsZP4zN5o0J9aAwBW75F7oUGzdzILdhIfG8mBneTATuIzYyNOCpAbM/3hNjN2Ep+ZGw3qWrEGweo9uViefNTLo7kwZu5kJuwkPjaSAzvJgZ3EZ7ZGnBQgIqI6OVpQDAAo13SM/3ibKbYmJCIiIrIqTgoQEVGdbE7LN/5dUSq2JiQiIiIiOXFSgAw2mw1dunQx1UqaZsRO4jN7o4TYP7Yi1HV5tyY0eyezYCfxsZEc2EkO7CQ+MzYyz5VQg/Dz8/P2EMgD7CQ+MzdKjItAn46tAAB3XBaFxLgIL4+o/szcyUzYSXxsJAd2kgM7ic9sjTgpQAZN05CcnAxN07w9FKoBO4nPCo06hDYDAMSEN/fySOrPCp3MgJ3Ex0ZyYCc5sJP4zNiIkwJERFRnfvaK/32UqbqXR0JEREREF4KTAkREVGe+ZycFSsvNM0tOREREZEWcFCAiojrz83HdKcBJASIiIiKZcVKADDabDfHx8aZaSdOM2El8VmjkulOgTOI7BazQyQzYSXxsJAd2kgM7ic+MjcxzJdQgnE6nt4dAHmAn8Zm9kZ9dAQA4Jb9TwOydzIKdxMdGcmAnObCT+MzWiJMCZNA0Dfv27TPVSppmxE7is0Ij404BiScFrNDJDNhJfGwkB3aSAzuJz4yNOClARER15lpTwFnO3QeIiIiIZMZJASIiqjPXnQKyPz5AREREZHWcFCA3drvd20MgD7CT+MzeyNdH/oUGAfN3Mgt2Eh8byYGd5MBO4jNbI0XXdd772cgKCwsRHByMgoICBAUFeXs4REQX7LOtWXjii50Y1LU1Foy+zNvDISIiIqLzePp9KO8UIIOu6ygsLATnicTGTuKzQiNfH/l3H7BCJzNgJ/GxkRzYSQ7sJD4zNuKkABk0TUNaWpqpVtI0I3YSnxUaGWsKSPz4gBU6mQE7iY+N5MBOcmAn8ZmxEScFiIiozsywJSERERERcVKAiIjqwbUlYZlqnlvniIiIiKyIkwLkxuFweHsI5AF2Ep/ZG/mZ4PEBwPydzIKdxMdGcmAnObCT+MzWiLsPNAHuPkBEZvNzej5un5uEmLDm+HHyNd4eDhERERGdh7sPUJ1pmoa8vDxTLZphRuwkPis0cj0+IPPuA1boZAbsJD42kgM7yYGdxGfGRpwUIIOu68jKyjLV9hpmxE7is0IjX/vZLQklfnzACp3MgJ3Ex0ZyYCc5sJP4zNiIkwJERFRnftx9gIiIiMgUOClARER1xt0HiIiIiMyBkwLkJjAw0NtDIA+wk/jM3sjXLv+aAoD5O5kFO4mPjeTATnJgJ/GZrRF3H2gC3H2AiMzmWFEpLpv2AwAgffr1UBTFyyMiIiIionNx9wGqM03TkJ2dbaqVNM2IncRnhUauNQUAoFyTc27ZCp3MgJ3Ex0ZyYCc5sJP4zNiIkwJk0HUd2dnZplpJ04zYSXxWaORaUwCQd7FBK3QyA3YSHxvJgZ3kwE7iM2MjTgoQEVGdubYkBOTelpCIiIjI6jgpQEREdWa3KXAtIyD7YoNEREREVsZJATIoioKQkBAuGCY4dhKfFRopimKsKyDrtoRW6GQG7CQ+NpIDO8mBncRnxkY+3h4AicNms6FDhw7eHgbVgp3EZ5VGfnYbSss1aR8fsEon2bGT+NhIDuwkB3YSnxkb8U4BMmiahszMTFOtpGlG7CQ+qzTy9XHdKSDndVqlk+zYSXxsJAd2kgM7ic+MjTgpQAZd15Gfn2+qlTTNiJ3EZ5VGrsUGZb1TwCqdZMdO4mMjObCTHNhJfGZsxEkBIiKqFz/J7xQgIiIiIk4KEBFRPfmeXWhQ1jsFiIiIiIiTAnQORVEQGRlpqpU0zYidxGeVRmbYfcAKnWTHTuJjIzmwkxzYSXxmbMTdB8hgs9kQGRnp7WFQLdhJfFZpJPvjA1bpJDt2Eh8byYGd5MBO4jNjI94pQAZVVZGamgpVVb09FKoBO4nPKo1cjw+USvr4gFU6yY6dxMdGcmAnObCT+MzYiJMC5KaoqMjbQyAPsJP4rNDItfuArHcKANboZAbsJD42kgM7yYGdxGe2RpwUICKievHzsQOQe1KAiIiIyOo4KUBERPXid/ZOAe4+QERERCQvTgqQQVEUREVFmWolTTNiJ/FZpZGvXe6FBq3SSXbsJD42kgM7yYGdxGfGRtx9gAw2mw2hoaHeHgbVgp3EZ5VGrkkBp6RbElqlk+zYSXxsJAd2kgM7ic+MjXinABlUVcXevXtNtZKmGbGT+KzSSPYtCa3SSXbsJD42kgM7yYGdxGfGRpwUIDclJSXeHgJ5gJ3EZ4VGxp0CEq8pYIVOZsBO4mMjObCTHNhJfGZrxEkBIiKqFz8TbElIREREZHWcFCAionpxPT7g5KQAERERkbQ4KUAGm82GmJgY2Gz8YyEydhKfVRrJ/viAVTrJjp3Ex0ZyYCc5sJP4zNiIuw+QQVEUBAUFeXsYVAt2Ep9VGplhS0IrdJIdO4mPjeTATnJgJ/GZsZF5pjfogqmqiuTkZFOtpGlG7CQ+qzQydh8ol3NLQqt0kh07iY+N5MBOcmAn8ZmxEScFyI2Z/nCbGTuJzwqN/OzyrylghU5mwE7iYyM5sJMc2El8ZmvESQEiIqoX37O7D8g8KUBERERkdZwUICKievE1Hh/gpAARERGRrDgpQAabzYYuXbqYaiVNM2In8VmlkeyPD1ilk+zYSXxsJAd2kgM7ic+MjcxzJdQg/Pz8vD0E8gA7ic8KjYyFBiWdFACs0ckM2El8bCQHdpIDO4nPbI04KUAGTdOQnJwMTZP3L/hWwE7is0ojY0tCSXcfsEon2bGT+NhIDuwkB3YSnxkbcVKAiIjqRfbHB4iIiIiIkwJERFRProUGnVxokIiIiEhanBQgIqJ6cW1JKPOaAkRERERWp+i6LufDoBIpLCxEcHAwCgoKEBQU5O3hVEvXdWiaBpvNBkVRvD0cqgY7ic8qjX7JyMfIOUnoFNoMa/8x0NvDqTOrdJIdO4mPjeTATnJgJ/HJ1MjT70N5pwC5cTqd3h4CeYCdxGeFRn4meHzACp3MgJ3Ex0ZyYCc5sJP4zNaIkwJk0DQN+/btM9VKmmbETuKzSiNfY6FBOW84s0on2bGT+NhIDuwkB3YSnxkbcVKAiIjqxdiSkGsKEBEREUmLkwJERFQv/iZ4fICIiIjI6jgpQG7sdru3h0AeYCfxWaGRGe4UsEInM2An8bGRHNhJDuwkPrM14u4DTUCW3QeIiOoi71Qper/yAwAg7dXrYbOJvQIvERERkZVw9wGqM13XUVhYCM4TiY2dxGeVRq7dBwCgTMLFdqzSSXbsJD42kgM7yYGdxGfGRpwUIIOmaUhLSzPVSppmxE7is0oj1+MDgJzrClilk+zYSXxsJAd2kgM7ic+MjTgpQERE9XLupECZpNsSEhEREVkdJwWIiKhe7DYF9rPrCMi82CARERGRlUk5KTBt2jRcccUVaNasGVq2bFnlOZmZmbjxxhvRvHlzhIWF4ZFHHoHT6XQ7Jzk5GQMGDEBAQADatWuHl156qdKzIevWrUPv3r3hcDgQExODOXPmNNZlCcHhcHh7COQBdhKfVRr52eXeltAqnWTHTuJjIzmwkxzYSXxma+Tj7QHUh9PpxG233YaEhAQsWLCg0vtVVcWwYcMQHh6OjRs3Ii8vD/fccw90XcesWbMAVKzEmJiYiIEDB2Lr1q3Yv38/Ro8ejebNm2PSpEkAgPT0dFx//fUYO3YsPvroI2zatAnjx49HeHg4RowY0aTX3BTsdju6du3q7WFQLdhJfFZq5GtXUFwGOCW8U8BKnWTGTuJjIzmwkxzYSXxmbCTlpMDUqVMBAIsWLary/StXrkRKSgqysrLQtm1bAMCbb76J0aNHY9q0aQgKCsLHH3+MkpISLFq0CP7+/ujevTv279+Pt956CxMnToSiKJgzZw46dOiAd955BwDwpz/9Cb/88gtmzJhR46RAaWkpSktLjV8XFhYCqJisUFUVAKAoCmw2GzRNc7s7wXXcdV5tx202GxRFqfI4gEoLYFR33G63Q1VV5Ofno2XLlsZ5dru90hirOy7iNem6XuVxma+JncS/JkVRkJ+fj+DgYOMc2a+puk6uHQhKneVun0OGa7JSJ5mviZ3EvyZN01BYWFjl3ZuyXpNrjOwk9jW5xshOYl+Ta4xm6aRpGk6ePInQ0FAoiiL0NXm6Q4KUkwK1SUpKQvfu3Y0JAQAYMmQISktL8euvv2LgwIFISkrCgAED4O/v73bOlClTkJGRgejoaCQlJWHw4MFurz1kyBAsWLAAZWVl8PX1rfLzT58+3Zi4ONfu3bvRokULAEBISAg6dOiAQ4cOIT8/3zgnMjISkZGRyMjIQFFRkXE8KioKoaGhOHDgAEpKSozjMTExCAoKQkpKitsfgi5dusDPzw/JycluY4iPj4fT6cS+ffuMY3a7HfHx8SgqKsLOnTsREhICRVHgcDjQtWtXnDhxAllZWcb5gYGBiI2NRW5uLrKzs43jol5TWlqacdws18ROYl9TXFwc0tLS4OvrC0VRTHFN1XVyPT6Qsm8/nMf8pLomK3WS+ZrYSfxrUhQFuq7DbrcjIyPDFNfETnJcEzvJcU1m66TrOk6cOIEBAwbg1KlTQl9TmzZt4AlFr+MGi3v27MGPP/6In376CYcOHUJeXh4URUFoaCiioqJwxRVXYNCgQbj44ovr8rL1smjRIjz22GM4efKk2/H7778fGRkZWLlypdtxf39/LFq0CH/9618xePBgdOrUCf/617+M9x85cgTt2rXDTz/9hISEBFx88cUYPXo0nn76aeOcn376Cf3798eRI0eq/U2u6k6BqKgo5OfnIygoCIA4M10udrsd5eXlSE5ORrdu3WC3243josx0WXlG8twxspP41wQAO3fudGsk+zVV1+nat9bjYN4ZfHZ/X/Tu2EqqawKs00nmawLYSfRrUlUVKSkp6N69uzFxI/s1ucbITmJfk2uM7CT2NbnGaJZOqqpi9+7duOSSS4zxiHpNp06dQsuWLVFQUGB8H1oVj+8U+M9//oOZM2di27Zt6NGjB3r37o0///nPaNWqlTFbkp6ejvnz5+ORRx5Bv3798Oijj2LkyJEevf6LL75Y5U/Xz7V161b06dPHo9c7/4sIqJjVOff4+ee4QtT1nPP5+/u73YHgYrfb3f5CA/zxh6yqc5v6uKIoUBSl0jirG2Ndj3vrmqo6Lvs1sZPY16SqapWNahqj6NcEVN3JtS2hqld+n+jXZKVONY1R9Gtip/od5zXxmqo7zmviNTXUGOt63EzX5PpeUPRrqul71nPVOilQUFCAgQMHorS0FBMmTMCyZcsQFhZW48fk5ubi008/xTPPPIMZM2Zg7dq1ta7QOGHCBNxxxx01ntOpU6fahgug4naLLVu2uB07ceIEysrKEBERYZxz7q0brnEDqPUcHx8fhIaGejQW2QQGBnp7COQBdhKfVRq5JgVk3ZLQKp1kx07iYyM5sJMc2El8ZmtU66RAcHAwnn/+edx0003VzmCcr3Xr1nj44Ycxfvx4LFu2zKMtG8LCwmqdbPBUQkICpk2bhqNHjxq3+K9cuRL+/v7o3bu3cc7TTz8Np9MJPz8/45y2bdsakw8JCQlYunSp22uvXLkSffr0qXY9AZnZ7XbExsZ6exhUC3YSn5UauRYalHFLQit1khk7iY+N5MBOcmAn8ZmxkUff5Q8fPtyjCYFzFzsAKn7Dbr755vqNrAaZmZnYsWMHMjMzoaoqduzYgR07duDUqVMAgMGDByMuLg533303tm/fjtWrV2Py5MkYO3as8SzFnXfeCX9/f4wePRq7du3CV199hVdffdXYeQAAxo0bh4MHD2LixInYs2cPFi5ciAULFmDy5MkNfk0i0DQN2dnZVT7PSeJgJ/FZqZGfveK/lzLeKWClTjJjJ/GxkRzYSQ7sJD4zNvLsR/9nHTp0CIMGDaryfYWFhejRowfWrVvXIAOryfPPP49LL70UL7zwAk6dOoVLL70Ul156KX755RcAFZMR3333HRwOB/r374/bb78dw4cPx4wZM4zXCA4OxqpVq3Do0CH06dMH48ePx8SJEzFx4kTjnOjoaCxfvhxr165Fz5498fLLL2PmzJk1bkcoM13XkZ2d7fHWFeQd7CQ+KzVyPT7glHBSwEqdZMZO4mMjObCTHNhJfGZsVKctCc+cOYO1a9dWOq5pGu69914UFxejW7duDTW2ai1atAiLFi2q8ZwOHTpg2bJlNZ4THx+P9evX13jOgAEDsG3btroOkYjIEmR+fICIiIiI6jgpUBVd1zFq1CisXLkSP/zwQ4OtC0BEROL7Y6FB88yWExEREVlJnR4fOF92djauu+46/Pjjj1i7di369euHAwcONNTYqIkpioKQkBCPt64g72An8VmpkZ/Euw9YqZPM2El8bCQHdpIDO4nPjI3qfafAggULMGXKFFxyySX4+eef0b59e1M9V2FFNpsNHTp08PYwqBbsJD4rNco7XQoA+Cn1OADgt6yT8Pe1obRMc3vr72NH/4vCkBgX4c3hurFSJ5mxk/jYSA7sJAd2Ep8ZG9V5UkDXdXTr1g0nT57EtGnTMHbs2MYYF3mBpmk4dOgQ2rdv7/H2k9T02El8Vmm0KiUHm9PyAQDf787B97tzajx/0U8ZmDeqjzATA1bpJDt2Eh8byYGd5MBO4jNjo1qvIisry/jn8OHDAIAbb7wRa9euxdChQ93eDwBHjhxx+zXJQ9d15Ofn844PwbGT+KzSKCk1D7Y63jn31qp9WJVS8+RBU7FKJ9mxk/jYSA7sJAd2Ep8ZG9V6p0DHjh0rPS/x+uuv44033qjy/GuvvRa6rkNRFKiq2jCjJCIi4STEhmLhpnTYFQXq2f8x2gBoABQA+jlvXfYcLcLYxb8IdccAERERkZXVOinwyy+/GP+enp6O2267DdOmTcPgwYPdJgt0Xcdll12Gzz//HJ06dWqUwRIRkTgS4yIwb1QfbE7LQ7+YUADA5rQ8OHztKClT3d5+t/MIMvLOAADsSsV5nBQgIiIi8r5aJwV69epl/HuLFi0AAG+88Qa++eYbvP7667j66qvdzo+Pj0fnzp0beJjUFBRFQWRkpKlW0jQjdhKflRolxkW4fXNf3Tf6PaNaYuziiklmVYcxieBNVuokM3YSHxvJgZ3kwE7iM2OjOq+MoCgKUlNTcdVVV2HIkCGYMGECiouLG2Ns1MRsNhsiIyNNs2CGWbGT+NiossS4CPTp2AoAMKJXOyHuEmAnObCT+NhIDuwkB3YSnxkb1etKWrVqhTfeeAObN2/G2rVr0a9fP2MRQpKXqqpITU3lWhCCYyfxsVHVBnZtDQBQNTEW5mEnObCT+NhIDuwkB3YSnxkbXdD0Ro8ePfDLL7+gffv2SEhIwPr16xEbG9tQYyMvKCoq8vYQyAPsJD42quxPbQIBVCw2KAp2kgM7iY+N5MBOcmAn8Zmt0QXf8+BwOPDNN9+gd+/eeOCBB3D69OmGGBcREZlM18ggAEDqsVMoLTfP7DoRERGRzOo0KeBwONC3b99Kx318fPDRRx9B0zSsWbOmwQZHRETm0SbYgeAAX5RrOn7PPeXt4RARERERPNh94FwdOnTATz/9VOX7mjdvjq1btxo7FJB8FEVBVFSUqVbSNCN2Eh8bVU1RFHSNDMSW9HzsOVqEbm2DvT4edhIfO4mPjeTATnJgJ/GZsZHHdwqcOXMGX331VY3n7NixA19++SXS09MveGDU9Gw2G0JDQ021kqYZsZP42Kh6f2pT8QjB3qOFXh4JO8mCncTHRnJgJzmwk/jM2MijKzl58iR27NiBkSNHQtd1HDp0CLruvnq0qqpYtGgR3n77bVxyySUYP358owyYGo+qqti7d6+pVtI0I3YSHxtVz1hsMNv7kwLsJAd2Eh8byYGd5MBO4jNjI48eH7j33nuRm5tr/PrBBx/Ezp07MXbsWIwZMwbt2rWD3W7H/PnzAQBr167FTTfdhPfff79xRk2NpqSkxNtDIA+wk/jYqGquOwV+yTiBqUt3o6RMhcPX7va2T6eQs+fkV3pfXd/W9Fr+PjYcyTmGtvvLUVqu1es1GnI8Vb3VdaD/RWFIjIvwQi1x8OtJfGwkB3aSAzuJz2yNap0U+PLLL/Hzzz/j888/R//+/QEAixYtwocffogFCxbglVdeweTJk/Hss8/C4XCgpKQEb731Fvr06dPogyciIrkcyi8GAJSWa/j3powqz1nyc1aDfT6PXiv14IW/hofq81qLfsrAvFF9LD8xQERERI2j1scH3n77bbz99tto3bq1cSw0NBSPPfYYkpOTMX/+fCxevBhxcXGYOnUq4uLisG3bNsyZM6dRB05ERPL55eAJmGdZnqZhU4DNaXneHgYRERGZVK13CnzxxRdo3bo10tLSAAClpaU4cuQIDh48iL1792Lr1q3w8/NDWloapk6dih49emD16tUICQlp9MFTw7LZbIiJiTHVohlmxE7iY6PqJcSGYuGmdNgAaAAUAPo5b20KoJ1dsqa6czx92xCv5e3xABUf0y8mtK6/1abBryfxsZEc2EkO7CQ+MzZS9PNXDDxP165dUVZWhjNnziAnJwc2mw2apsFut6Nt27bo2bMnrrrqKlx11VXYsGEDpkyZgvXr1yMhIaGprkF4hYWFCA4ORkFBAYKCgrw9HCIir1qVkoPNaXnVPkvv+ga4pnM8fdsQr+Wt8WxOO47dR4owLL4N/u+uXt6JRURERNLy9PvQWu8UGDduHPz9/XHy5Ek8++yzWL16NaKjo43FBc/Vt29fHD9+HH/729/w22+/oUWLFhd+JdRkVFVFSkoK4uLiKrUlcbCT+NioZolxER49H9+Qz9BX9Vp/dOriUafGHs/5/rU+FbuP7IXNZu0HLvj1JD42kgM7yYGdxGfGRrXe8/DYY4/hwQcfxF/+8hcAwNVXX40OHTrg8OHDyMnJwcmTJ1FaWmqcP336dISGhmLChAmNN2pqNGbaWsPM2El8bCQHkTt1Cm0OAMg4ftrLI/E+kTtRBTaSAzvJgZ3EZ7ZG9X4QolOnTmjbti1CQ0PRrFkzOBwOxMbGYs6cOZgzZw5atWrVkOMkIiKylE5hZycF8k6jlif9iIiIiOqt1scHarJt2zYEBQWhuLgYBQUFWLFiBZ566ikUFBSgVy8+/0hERFRfHUKaQVGAopJy5J92IrSFv7eHRERERCZ0QZMCbdu2RXh4uPHryMhITJs27YIHRd5hs9nQpUsXU62kaUbsJD42koPonRy+drQJcuBIQQky8s5YdlJA9E7ERrJgJzmwk/jM2KjOV6IoivHW9e/nvu+ZZ55pmJGRV/j5+Xl7COQBdhIfG8lB9E6uRwgO5ll7XQHROxEbyYKd5MBO4jNbozpNCui6jg4dOqBjx47QdR29e/dGXFwc+vfvjzvvvBMffPABrrvuOpSXlzfWeKkRaZqG5ORkaJrm7aFQDdhJfGwkBxk6deRig1J0sjo2kgM7yYGdxGfGRh4/PhAWFob33nvP7Ziqqjh58iTy8/ORn5+P7777Dq+88gpCQ0Px5JNP4qGHHjLdLAoREVFT6RTaDACQkXfGyyMhIiIis/J4UiAoKAjjx4+v9byTJ09i9uzZWLt2LR599NELGhwREZGVnbsDAREREVFjuKCFBqvSsmVLTJkypaFfloiIyHI6nX18IP14xbaE56/lQ0RERHShal1TQFVVPPvss8jNza3zix85cgTPP/98vQZGTc9msyE+Pt5UK2maETuJj43kIEOnjmcfHygqKcfX2w97eTTeIUMnq2MjObCTHNhJfGZsVOuVlJaWIj8/HxdffDFGjx6N77//HsXFxdWef+bMGXz33Xe4++67ERcXh9OnT8PpdDbooKnxsJUc2El8bCQH0TttOHDc+PfHP/sNq1JyvDga7xG9E7GRLNhJDuwkPrM1qnVSoFmzZnj//fexdetWhIWF4e6770ZQUBC6d++OG264AXfddRfuuusuDBs2DHFxcQgODsbYsWPRrl077NixA2+++SYXG5SEpmnYt2+fqVbSNCN2Eh8byUGGTkmpeca/2xRgc1peDWebkwydrI6N5MBOcmAn8ZmxkcdrCnTu3BkzZszA66+/jp07d2Lz5s04dOgQ8vPzoSgKYmJiMHz4cCQkJKBbt2587pGIiOgCJcSGYuGmdACApgP9YkK9PCIiIiIymzovNGiz2dCzZ0/07NmzEYZDRERELolxEYgJa46046cx/ppYJMZFeHtIREREZDLmWR2BGoTdbvf2EMgD7CQ+NpKDDJ3CWvgDALq3C/bySLxHhk5Wx0ZyYCc5sJP4zNZI0XVd9/YgzK6wsBDBwcEoKChAUFCQt4dDREQSuXPeZvyUmod37+iJm3u28/ZwiIiISBKefh/KOwXIoOs6CgsLwXkisbGT+NhIDrJ08rVX/K+6TBV7nI1Flk5WxkZyYCc5sJP4zNiIkwJk0DQNaWlpplpJ04zYSXxsJAdZOvnaKxbuLVfFHmdjkaWTlbGRHNhJDuwkPjM2apRJgZKSErz99tuN8dJERESW4mNz3Slgnr98EBERkTg8mhQ4evQogoM9X+DozJkzmDx5cr0HRURERBV8faz9+AARERE1Lo+2JNR1HadOnQIABAYG1nqrhJlupbAah8Ph7SGQB9hJfGwkBxk6+drOPj5g4f+3ytDJ6thIDuwkB3YSn9ka1fnxgdLSUmzYsAHr16+H3W7Hhg0b0KxZM2zYsAF+fn7YsGEDli1b1hhjpUZmt9vRtWtX022xYTbsJD42koMsnXzOrilg1TsFZOlkZWwkB3aSAzuJz4yN6rWmQK9evdC7d2/4+vqiV69e8PPzQ69evYxfX3rppQ09TmoCmqYhLy+Pd3oIjp3Ex0ZykKWTj93aawrI0snK2EgO7CQHdhKfGRvVOilwww034PnnnwcA5OXlNfqAyHt0XUdWVpapttcwI3YSHxvJQZZOfmcnBcoteqeALJ2sjI3kwE5yYCfxmbFRjZMCqqoiKioKmZmZ0HUdbdu2haqqTTU2IiIiy/M5u6ZAmYl+IkFERETiqHGhQbvdjtmzZ+Pw4cPo0KED0tLS0LFjx1pf1EyzJkRERN5kPD5Qzv+3EhERUcPzaE0BRan4KUW7du1gs1X/Ia7zXG9JPoGBgd4eAnmAncTHRnKQoZOfnbsPyNDJ6thIDuwkB3YSn9kaebQl4bl0XYe/vz8AoKysDP7+/pXemmnRBSux2+2IjY319jCoFuwkPjaSgyyd/lho0Jp3CsjSycrYSA7sJAd2Ep8ZG3k8KeB6JGD37t21npufn4/+/fvXf1TkFZqmITc3F61bt67xjhDyLnYSHxvJQZZOri0Jyy28+4AMnayMjeTATnJgJ/GZsVGtkwLDhg3DVVddhWPHjgEALr744lpftKioCAMGDLjw0VGT0nUd2dnZCA8P9/ZQqAbsJD42koMsnXxt1t6SUJZOVsZGcmAnObCT+MzYqNZJgb///e9488038eabb+Kxxx5Dr169PHrhf/zjHxc8OCIiIqvztbt2H7Dm4wNERETUuGqdFBgxYgRGjBiBTZs2YcaMGXjuueegKAratWsHH5/qP1xRFKSmpjboYImIiKzGtaaAVR8fICIiosbl8ZoC/fv3R//+/fHDDz9g3LhxCA0NxYcffujR4wQkB0VREBISwt0jBMdO4mMjOcjSybhTwKILDcrSycrYSA7sJAd2Ep8ZG9V5ZYTrrrsO27dvR7t27fD22283xpjIS2w2Gzp06GCaBTPMip3Ex0ZykKWTj8XXFJClk5WxkRzYSQ7sJD4zNqrXlQQGBuLLL7/E7NmzG3o85EWapiEzM5NbSgqOncTHRnKQpZOvj+vxAWveKSBLJytjIzmwkxzYSXxmbFSnSYGbb74ZR44cqfW8goIC9OnTB1u3bq33wKjp6bqO/Px8Y/tJEhM7iY+N5CBLJ1/b2S0JTfSXj7qQpZOVsZEc2EkO7CQ+MzbyeE0BAFi6dCliY2Nx7bXXomfPnmjfvn2lczRNw913343Tp0+ja9euDTZQIiIiK3ItNOi06J0CRERE1Ljq/PjArl278OSTTyImJgZhYWEYNWoUVqxYAU3TkJGRgf79+yM5ORlLly5FYGBgY4yZiIjIMlwLDXL3ASIiImoMdZ4U+L//+z/s3r0bp0+fxrJly9CxY0c89thjaNOmDXr27Im2bdtix44duOiiixpjvNSIFEVBZGSkqVbSNCN2Eh8byUGWTr52a68pIEsnK2MjObCTHNhJfGZsVOvjA//973/RqlUrXHbZZQAqfhN0Xcfvv/+Obdu2YfPmzcjMzMQ111yD33//HcePHzfV8xVWYrPZEBkZ6e1hUC3YSXxsJAdZOvmcXVOgzKJrCsjSycrYSA7sJAd2Ep8ZG9V6p8DatWtx9913IyQkBAAwcuRIBAUFYdCgQdi4cSNuu+02HDx4EMuXL8fWrVvRrFkz9OvXz6MFCUksqqoiNTUVqqp6eyhUA3YSHxvJQZZOrjUFrLoloSydrIyN5MBOcmAn8ZmxUa2TAu+99x6OHDmC9PR0fP7557j66qsREBCAkJAQJCYmYvTo0QgPDwcABAcHY/ny5ejcuTNGjRrV6IOnhldUVOTtIZAH2El8bCQHGTr5WfzxAUCOTlbHRnJgJzmwk/jM1qjWSYG8vDwAgL+/Pz7//HPMnDkTO3fuxGOPPYYXX3wRa9euhdPpNM5XFAX/+te/sH37dsycObPxRk5ERGQBPmcXGiyz8KQAERERNZ5aJwWmT5+O6667DhkZGUhKSoKqqrjppptw+vRpY7eByMhITJgwAYcOHQIApKWl4ZNPPsHtt9/e6BdARERkZr7GpIA1Hx8gIiKixlXrpMBrr72GoUOHYs6cOQCAV199FUFBQZgwYQIURYGqqjh58iRKSkpwySWXYO7cubjxxhsREhJiugUYzE5RFERFRZlqJU0zYifxsZEcZOn0x+4D1pwUkKWTlbGRHNhJDuwkPjM2UvRatgr4/PPPAQC5ubl44oknYLPZ8MYbbyA0NBQDBgyAw+FAq1atoKoqvv32W4waNQqJiYnGxxFQWFiI4OBgFBQUICgoyNvDISIiiRw+WYz+r/0IPx8b9r/yZ28Ph4iIiCTh6fehtW5JOH36dACA0+lEcXExFEXBrFmz4O/vj9zcXCxfvtw4t1evXgCA9evX49ixY8YChCQHVVVx4MABdO7cGXa73dvDoWqwk/jYSA6ydPI9uyWhVe8UkKWTlbGRHNhJDuwkPjM2qnVSYNu2bSguLsaWLVtw++2349JLL4XT6cS3336L06dP4+DBg/jf//6HxMREhISE4O6770ZpaSn+8Y9/YNGiRU1wCdSQSkpKvD0E8gA7iY+N5CBDJ9eWhJoOqJoOu808tyt6SoZOVsdGcmAnObCT+MzWqNY1BX788UdcdNFFWLhwIQICAvDdd9+hdevWGDJkCMLCwvDMM88AAAYMGIDly5dj4sSJeOmll/DFF1/g5MmTjT1+IiIiU3MtNAhwsUEiIiJqeLVOCgQGBuK7777Dyy+/DF3X4ePjg4ULFyInJwfPPvssbDYbWrZsiWeffRaZmZmIjo5GZGQkEhIS8M033zTFNRAREZmWa6FBACjXuC0hERERNaxaHx+47LLLAAClpaVYsmQJAKB58+ZYunQp2rdvj8DAQOTn5wMAWrVqZXzce++9h9jY2MYYMzUSm82GmJgY2Gy1zhWRF7GT+NhIDrJ08jnncYGycg3w9+JgvECWTlbGRnJgJzmwk/jM2KjWSQEXf39/9O/f3/h1XFxcjedffPHF9R8VeYWiKNwdQQLsJD42koMsnc5dQ6BMs97jA7J0sjI2kgM7yYGdxGfGRh5PCgDA0qVLsWPHDo/Pf+655+o6HvIiVVWRkpKCuLg406ykaUbsJD42koMsnRRFgZ/dBqeqoVy13uMDsnSyMjaSAzvJgZ3EZ8ZGdZoU2LNnD9asWVPreevWrcPll1/OSQEJqarq7SGQB9hJfGwkB1k6+dgVOFVYclIAkKeTlbGRHNhJDuwkPrM18mhS4IsvvsCIESPwxBNPYNCgQejdu7fb+0tKSvDVV1/hr3/9K4CK5ywWL17c8KMlIiKyINe6Ak7uPkBEREQNrNbVETRNw+23347PP/8cADBmzBiMGTMG5eXlACoWIBw2bBjeeecdFBcXN+5oiYiILMjPp+J/1+UWXFOAiIiIGletkwI2mw26rmPixIkoLi7Gxo0bUVxcjMTERBw/fhy33XYbVFXF6tWrERAQ0BRjpkZis9nQpUsXU62kaUbsJD42koNMnXzOjtGKjw/I1Mmq2EgO7CQHdhKfGRt5fCX9+/fH7NmzERQUhE8++QTXXHMNYmJikJ2dje+++w4tWrRozHFSE/Hz8/P2EMgD7CQ+NpKDLJ187BWPD5RZ9PEBWTpZGRvJgZ3kwE7iM1sjjyYFFEXB+PHjMXPmTGzYsAEbNmwwjj/55JPYtm0bNmzYgMzMzEYdLDUuTdOQnJwMjbenCo2dxMdGcpCpk6+94n/XZRa8U0CmTlbFRnJgJzmwk/jM2Mjj3QeioqJw5ZVXYtCgQcax8vJyY3FBXdfh5+eHtLS0hh8lERGRhfmevVOg3KJ3ChAREVHj8ehOAV2v+MnERx99BKfTafwzbtw4PPDAA8avIyMjsXfv3kYdMBERkdW41hQo06x3pwARERE1rnqtjnDkyBHcc889ePrpp/HNN99gxYoVUBQFUVFRCA4ObugxEhERWZrrToGyct4pQERERA2rzpMCGRkZ6NevHwIDA9GmTRu89957+Pvf/478/HysXbsWPXv2bIRhUlOw2WyIj4831UqaZsRO4mMjOcjUybWmgBW3JJSpk1WxkRzYSQ7sJD4zNqrzlUyaNAkPPPAA3nvvPdjtdtx0003o168f7rvvvsYYHzUxp9Pp7SGQB9hJfGwkB1k6/bH7gDUfH5Clk5WxkRzYSQ7sJD6zNfJoUmDkyJFo3rw5gIp1BZ555hm397/33nsYP3688WtFURpwiNRUNE3Dvn37TLWSphmxk/jYSA4ydbLynQIydbIqNpIDO8mBncRnxkYe7T7w2WefAQDGjx+PuXPnNuqAiIiIyJ2PzbWmgDXvFCAiIqLG4/GWhAAwceJE3HHHHR6d26FDh3oNiIiIiNy57hQoM9FPJYiIiEgMdZoUuOiii3DRRRc11lhIAHa73dtDIA+wk/jYSA6ydDIeH7DomgKydLIyNpIDO8mBncRntkb1WjLxhRdeQH5+fqXjZ86cwVdffYWDBw9e8MCo6dntdsTHx5vuD7nZsJP42EgOMnX6Y6FB690pIFMnq2IjObCTHNhJfGZs5NGkwAMPPIDc3Fzj16+88gry8vKMX69ZswYjRoxAaGgoHnjgAWzdurXhR0qNTtd1FBYWQtet+ZMoWbCT+NhIDjJ18jm77ZEVdx+QqZNVsZEc2EkO7CQ+MzbyaFJg/vz5KCgoMH597m/AlVdeiREjRiAqKgrr1q1DTk4ORo4c2fAjpUanaRrS0tJMtZKmGbGT+NhIDjJ18vOpuFOg3IJ3CsjUyarYSA7sJAd2Ep8ZG3m0pkBNsyCvv/46evXqBYfD0WCDIiIioj8Ydwpo5vmpBBEREYmhTgsNnuuDDz5AREQEAODXX391e5+vry8SExMRGxt7YaMjIiIiS68pQERERI2r3pMCn3/+Ofz9/at834kTJ/D+++9j586d9R4YeQfv+JADO4mPjeQgSyc/Y/cBa04KyNLJythIDuwkB3YSn9ka1XtSYNmyZejcuXOV79uzZw969OhR70GRd9jtdnTt2tXbw6BasJP42EgOMnX6404B6z0+IFMnq2IjObCTHNhJfGZsVK8tCc81ZswYvPLKKzhw4MAfL2qzoU+fPhf60tTENE1DXl6eqRbNMCN2Eh8byUGmTq41BcolGGtDk6mTVbGRHNhJDuwkPjM28mhS4I477kBgYGCV77vuuuvw008/oXv37hg0aBB++eUXdOnSBT/99FODDpQan67ryMrKMtX2GmbETuJjIznI1MnXdadAufhjbWgydbIqNpIDO8mBncRnxkYeTQr85z//QWRkJFatWoV9+/Zh/vz5iIyMxC+//IJbb70Vy5cvx6FDhxAXF4crr7wS33//fWOPm4iIyDJ87a7dB8zzUwkiIiISQ50eH5g+fTpWrlyJv//973jvvfcwdOhQ/O9//wMAhIeHY9asWdi1axcGDx7cKIN1mTZtGq644go0a9YMLVu2rPIcRVEq/TNnzhy3c5KTkzFgwAAEBASgXbt2eOmllyrN+Kxbtw69e/eGw+FATExMpdcgIiJqbD7GQoPm+akEERERiaHWhQaTk5ONfz916hSysrJw2223ITk5GR9++CHat2/vdg4A7Nq1CwAQHx/fwMOt4HQ6cdtttyEhIQELFiyo9rx///vfGDp0qPHr4OBg498LCwuRmJiIgQMHYuvWrdi/fz9Gjx6N5s2bY9KkSQCA9PR0XH/99Rg7diw++ugjbNq0CePHj0d4eDhGjBjRKNfmbdU9JkJiYSfxsZEcZOnka/EtCWXpZGVsJAd2kgM7ic9sjRS9lochbDYbFEUxfoLu+ndFUYxzqnoJRVGgqmoDD9fdokWL8Nhjj+HkyZNVfv6vvvoKw4cPr/JjZ8+ejSlTpiAnJ8fYWvG1117DrFmzcOjQISiKgieffBLffvst9uzZY3zcuHHj8NtvvyEpKcnjcRYWFiI4OBgFBQUICgqq0zUSEREt+TkTU75MxnV/isD8e7iQLxEREdXO0+9Da71TwPUNt67ruPHGGxEeHo6SkhKsWbMG119/PR544AFcfvnlbpMEopgwYQLuu+8+REdH495778X9998P29kVnJOSkjBgwABjQgAAhgwZgilTpiAjIwPR0dFISkqq9CjEkCFDsGDBApSVlcHX17fKz1taWorS0lLj14WFhQAAVVWNiRJFUWCz2aBpmtukiuv4+RMq1R13TdpUdRxApVUxqztut9uhqipycnIQHh5unGe32yuNsbrjIl6TrutVHpf5mthJ/GtSFAU5OTkICwszzpH9mtjJu9dkUypeu+zs/0fYSe5rMlsn10rc4eHhOJ+s1+QaIzuJfU2uMbKT2NfkGqNZOmmahmPHjiEyMhKKogh9TZ4uhljrpMC5Mwo+Pj645ppr8PDDDyM3Nxcff/wxxowZg06dOmHWrFm49NJLPfqkTeHll1/GoEGDEBAQgNWrV2PSpEk4fvw4nn32WQBAdnY2OnXq5PYxERERxvuio6ORnZ1tHDv3nPLychw/fhxt2rSp8nNPnz4dU6dOrXR89+7daNGiBQAgJCQEHTp0wKFDh5Cfn2+cExkZicjISGRkZKCoqMg4HhUVhdDQUBw4cAAlJSXG8ZiYGAQFBSElJcXtD0GXLl3g5+dX6dGO+Ph4OJ1O7Nu3zzhmt9sRHx+PoqIi7NmzBzk5OVAUBQ6HA127dsWJEyeQlZVlnB8YGIjY2Fjk5uYiOzvbOC7qNaWlpRnHzXJN7CT2NcXFxSErKwvZ2dnGhKns18RO3r2m7MOHAQAnC4qQnJzMTpJfk9k6ue4idTgcyMjIMMU1sZMc18ROclyT2Trpuo4TJ04gIiICp06dEvqaqvt+9Xy1Pj5wrjfffBOXXnoprr32WuNYSUkJXnzxRaxatQrff/89wsLCPH05Ny+++GKV30ifa+vWrejT54/bJmt6fKCqsb/00ksoKCgAAAwePBjR0dGYO3eucc7hw4fRvn17JCUloV+/frj44osxZswYTJkyxThn06ZNuPLKK3H06FFERkZW+bmqulMgKioK+fn5xiSLKDNdLna7HeXl5UhOTka3bt1gt9uN46LMdFl5RvLcMbKT+NcEADt37nRrJPs1sZN3r2npb4fxyCe/4fJOrbBkbF92kvyazNZJVVWkpKSge/fuxsSN7NfkGiM7iX1NrjGyk9jX5BqjWTqpqordu3fjkksuMcYj6jWdOnUKLVu2vPDHB87lWoDvXA6HA6+99hpee+21urxUJRMmTMAdd9xR4znn/2S/Lvr164fCwkLk5OQgIiICkZGRbrM0AJCbmwvgjzsGqjvHx8cHoaGh1X4uf39/t8cSXOx2u9tfaIA//pBVdW5TH1eUil0azh9ndWOs63FvXVNVx2W/JnYS+5pUVa2yUU1jFP2aAHaqz/GGGru/b8X/rss1vdI3xudjJ/GvCTBfJ4DXVJ/jvCZeU3XHeU1iX5Nrwkb0azp/Yqk6dZoUaExhYWH1vsvAE9u3b4fD4TC2MExISMDTTz8Np9MJPz8/AMDKlSvRtm1bY/IhISEBS5cudXudlStXok+fPtWuJyAzRVEQEhLi8R8e8g52Eh8byUGmTq7dB8o1621JKFMnq2IjObCTHNhJfGZsVPWUhOAyMzOxY8cOZGZmQlVV7NixAzt27MCpU6cAAEuXLsW8efOwa9cupKamYv78+XjmmWdw//33Gz/Bv/POO+Hv74/Ro0dj165d+Oqrr/Dqq69i4sSJRuBx48bh4MGDmDhxIvbs2YOFCxdiwYIFmDx5steuvTHZbDZ06NCh2pkqEgM7iY+N5CBTJ5+zY3SWW29LQpk6WRUbyYGd5MBO4jNjI4/uFDh/sT1P5eTk1OvjavP888/jgw8+MH7tWuBwzZo1uOaaa+Dr64v3338fEydOhKZpiImJwUsvvYSHHnrI+Jjg4GCsWrUKDz30EPr06YNWrVph4sSJmDhxonFOdHQ0li9fjscffxz/93//h7Zt22LmzJkYMWJEo1yXt2mahkOHDqF9+/am+kNuNuwkPjaSg0ydfCx8p4BMnayKjeTATnJgJ/GZsZFHkwLHjh3D9OnT0aZNG+i6jjFjxhi/Pn36NB566CG8++67CA4ORmFhIR555BG89957jTboRYsWYdGiRdW+f+jQoRg6dGitrxMfH4/169fXeM6AAQOwbdu2ug5RSrquIz8/H+3atfP2UKgG7CQ+NpKDTJ387BV/6ShXrXengEydrIqN5MBOcmAn8ZmxkcdrCtx6663o3LkzAGDMmDHGrwsLC41FAsPDw5Gfn49HH30Uf/3rXxtt0ERERFbic3ZSoEy13p0CRERE1LjMcb8DERGRifnYKh4fKLPgnQJERETUuBpkUuD8PRhJToqiIDIy0lQraZoRO4mPjeQgUyc/n7OPD1hwTQGZOlkVG8mBneTATuIzYyNhtiQk77PZbIiMjPT2MKgW7CQ+NpKDTJ2sfKeATJ2sio3kwE5yYCfxmbERHx8gg6qqSE1Nhaqq3h4K1YCdxMdGcpCpk6+x0KD17hSQqZNVsZEc2EkO7CQ+MzbipAC5KSoq8vYQyAPsJD42koMsnVxbElrxTgFAnk5WxkZyYCc5sJP4zNaIkwJERESCM+4U0HSu40NEREQNyuNJgfMXUjj314qi1Ph+IiIiqj9f2x//u7biYoNERETUeDxeaPDaa6+Fr69vpV9rmgZd19GnTx/Y7Xaoqgpd19GzZ09kZGQ0xpipkSiKgqioKE7oCI6dxMdGcpCpk+vxAaDiEQLXnQNWIFMnq2IjObCTHNhJfGZs5NGkwKJFixp5GCQCm82G0NBQbw+DasFO4mMjOcjU6dxJgDKLLTYoUyerYiM5sJMc2El8Zmzk0aTAqFGjGnscJABVVXHgwAF07twZdrvd28OharCT+NhIDjJ18j3nToFyiy02KFMnq2IjObCTHNhJfGZsZJ37D8kjJSUl3h4CeYCdxMdGcpClk6IosNsqJgasuKaALJ2sjI3kwE5yYCfxma0RJwWIiIgk4HN2UsBZbq07BYiIiKhxcVKAiIhIAn7nbEtIRERE1FA4KUAGm82GmJgY2Gz8YyEydhIfG8lBtk6uHQistqaAbJ2siI3kwE5yYCfxmbGRx1sSkvkpioKgoCBvD4NqwU7iYyM5yNbJ5+ydAk6LTQrI1smK2EgO7CQHdhKfGRuZZ3qDLpiqqkhOToaqqt4eCtWAncTHRnKQrZOva6FBi21JKFsnK2IjObCTHNhJfGZsxEkBcmOmP9xmxk7iYyM5yNTJ18e1poC17hQA5OpkVWwkB3aSAzuJz2yNOClAREQkAdfuA2UWu1OAiIiIGhcnBYiIiCTge3ZNgTKLrSlAREREjYuTAmSw2Wzo0qWLqVbSNCN2Eh8byUG2Tq5JAautKSBbJytiIzmwkxzYSXxmbGSeK6EG4efn5+0hkAfYSXxsJAeZOrm2JLTinQIydbIqNpIDO8mBncRntkbckpAMmqYhOTkZ8fHxsNvt3h4OVYOdxMdGcpCt06mScgDAl9sOYXNaPhJiQwEASal5CPCzo9ip1vttTa+VEBuKxLgIr123bJ2siI3kwE5yYCfxmbERJwWIiIgEtyolBwdyTwEAVuzOAQAs3JTeYK9f3WspZ983b1Qfr04MEBERUePh4wNERESCS0rNg+KFz6sDsCsKNqfleeGzExERUVPgpAAREZHgEmJDce7ygg05QVDba6m6jn4xoQ34GYmIiEgkiq7r1lrG2AsKCwsRHByMgoICBAUFeXs41dJ1HZqmwWazQVG88TMp8gQ7iY+N5CBbp1UpOdiclgeHrx0lZarxjfq5x+r7tqrX+nbHYRw75cT9V8fg6ev/5LXrlq2TFbGRHNhJDuwkPpkaefp9KNcUIDdOpxMOh8Pbw6BasJP42EgOMnVKjIuo8rn+hnzW/9zXKlM1LE46CFXz/s8OZOpkVWwkB3aSAzuJz2yN+PgAGTRNw759+6Bp1tvuSibsJD42kgM71ezSDi0BANszT3h1HOwkPjaSAzvJgZ3EZ8ZGnBQgIiKiSi6NagUA2HWkEKXlqpdHQ0RERI2FkwJERERUScfQZmjVzBfOcg17jhZ5ezhERETUSDgpQG7sdru3h0AeYCfxsZEc2Kl6iqLg0g4Vdwt4+xECdhIfG8mBneTATuIzWyPuPtAEZNl9gIiI6FyzVh/Am6v2IzqsOYbERaC4TEWAr73Gt32jK3Yy2JKehwA/O5zlOhJiQxt0QUQiIiKqHXcfoDrTdR1FRUUIDAwUfnsNK2Mn8bGRHNipdq6NB9KPn8ac9WkefcwHSQcrHVu4KR3zRvWp18QAO4mPjeTATnJgJ/GZsREnBcigaRrS0tIQHx9vultizISdxMdGcmCn2uWdLm2w13pn1X7syDqJYqeKAD97jW8TYivuNkhKzYPDR0HW0RxEtYlASble68fW9Fp1/ViZXsubd2Lwa0kO7CQHdhKfGRtxUoCIiIiqdFXncCxOOggbAA2AAkCv4a1N+ePuAtfHuOw+WojdRws9+rwLN6VXPrjfszsVPHqtehL5tep7JwYREREnBYiIiKhKiXERmDeqDzan5cHha0dJmVrr234xFT8Bd33MNzsO49CJYi9fifm98f1eJKUeR0JsGCcHiIioTjgpQG4cDoe3h0AeYCfxsZEc2Kl2iXER9fom0/UxPaNaYuziX4y7CWqjKIBrCWRPP6ba17rAj5fhtVz255zCgZxTWLgpwyt3DfBrSQ7sJAd2Ep/ZGnH3gSbA3QeIiMjKVqXkXNDdBp58jGVfy1mOjb8fx76cU8bvd7c2Qbima2uh1yEgIqLG5+n3oZwUaAKyTApomoYTJ06gVatWsNls3h4OVYOdxMdGcmAnObBT7Val5GDs4l/q/HENdUcBG8mBneTATuKTqZGn34eKfRXUpHRdR1ZWFjhPJDZ2Eh8byYGd5MBOtXOt/dAm2PPbWW1KxV0KDYGN5MBOcmAn8ZmxEdcUICIiIpKc6yf+nq7foOkwHmEgIiJr46QAERERkQl4sltEQbET//31MPx9bLj64jBvD5mIiATASQFyExgY6O0hkAfYSXxsJAd2kgM7ea623SI0TceGA8eRU1iKpNQ8XNOldYN8XjaSAzvJgZ3EZ7ZGXGiwCciy0CARERGZ39NfJeM/WzJxV98OmHZLvLeHQ0REjcTT70N5pwAZNE1Dbm4uWrduLfxKmlbGTuJjIzmwkxzYqeElxkXgP1sy8dX2wwgO8EVJmYaE2Ir1BZJS86rdxrC6tw5fBfkFpxAS3AIlZXqVr8UtEL2PX0tyYCfxmbERJwXIoOs6srOzER4e7u2hUA3YSXxsJAd2kgM7NbxipwoAOONU8f7aVADAwk3pDfDKuVW+lnL2WENtgUj1w68lObCT+MzYyBxTG0RERETkkV8yTkBpws+no2G3QCQioobFSQEiIiIiC0mIDa34Rv3sr23nzBC4jil1fGt8fBWvBVRsgZhx/DRWpeTUd9hERNRI+PgAGRRFQUhICBSlKX9+QHXFTuJjIzmwkxzYqeFVtXVhv5iKdQBq2s6wurf+PgqOnyhAWKtglJbrbq9Vrmn4MOkgNB34cV8uVu/N5WMEXsKvJTmwk/jM2Ii7DzQB7j5AREREVvWXuUnYkp4PALArwOj+0Xjuhjgvj4qIyPw8/T6Ujw+QQdM0ZGZmQtM0bw+FasBO4mMjObCTHNhJfLU1GtO/k/Hvqg7jTgJqWvxakgM7ic+Mjfj4ABl0XUd+fj7atWvn7aFQDdhJfGwkB3aSAzuJr7ZGQ7u3waTBF+PNlfsBAMuTj2BH1skqtzfk1oWNh19LcmAn8ZmxEScFiIiIiKhRPXxtZ6zZm4ttmSfx1fYj1Z7HrQuJiJoeHx8gIiIiokYXG96i1nMUbl1IRNTkOClABkVREBkZaaqVNM2IncTHRnJgJzmwk/g8bTS4W2TF+TWco3PNgUbDryU5sJP4zNiIuw80Ae4+QERERASsSsmpctvDYmc5/vNzFnxsCna8MBgt/PmEKxHRhfL0+1D+F5cMqqoiIyMDnTp1gt1u9/ZwqBrsJD42kgM7yYGdxFeXRolxEVWuF6DrOn5KzUNG3hlsPHAMQ7u3aazhWha/luTATuIzYyM+PkBuioqKvD0E8gA7iY+N5MBOcmAn8V1oI0VRcG3XismC1XtyG2JIVAV+LcmBncRntka8U4CIiIiIvO66P7XGwk3pWLrzCMID/VFSpiEhtmJ9gaTUPG5XSETUSDgpQEREREReV1BcBgAoKdPw/tpUABVbFAKATeF2hUREjYWPD5BBURRERUWZaiVNM2In8bGRHNhJDuwkvoZqtDXjRLU7E2g6YFcUbld4Afi1JAd2Ep8ZG3FSgAw2mw2hoaGw2fjHQmTsJD42kgM7yYGdxNdQjRJiQ6Hjjy0Lz//7tqrr3K7wAvBrSQ7sJD4zNjLPldAFU1UVe/fuhaqq3h4K1YCdxMdGcmAnObCT+BqqUWJcBOaN6oO/XxmNhwZehL/3j8a8UX1w1UVhAIA+HVvx0YELwK8lObCT+MzYiGsKkJuSkhJvD4E8wE7iYyM5sJMc2El8DdWoqi0L27Z0YMPMjdiWeQJPfbkToc39UexUEeBnd3vLhQhrx68lObCT+MzWiJMCRERERCSsbm2DERveHKnHTuOTn7OqPY8LERIR1Q8fHyAiIiIioXUMbV7rOTYFXIiQiKgeOClABpvNhpiYGFMtmmFG7CQ+NpIDO8mBncTXFI3+enkHAKh2dwKgYocCLkRYPX4tyYGdxGfGRoqu67q3B2F2hYWFCA4ORkFBAYKCgrw9HCIiIiLprErJwea0PDh87SgpU423m9OOY/eRIgzpFoG5d/fx9jCJiITh6feh5pneoAumqiqSk5NNtZKmGbGT+NhIDuwkB3YSX1M1SoyLwHM3xOEfQ7q4vR2V0AkAUFhc3qifX3b8WpIDO4nPjI04KUBuzPSH28zYSXxsJAd2kgM7ic+bjS5p3xIAsOtwATSNN8DWhF9LcmAn8ZmtEScFiIiIiEhanVu3gMPXhqLScqTnnfb2cIiIpMNJASIiIiKSlo/dhm5tgwEAOw+d9O5giIgkxEkBMthsNnTp0sVUK2maETuJj43kwE5yYCfxidAovl3FpMBvWQVeG4PoROhEtWMn8ZmxkXmuhBqEn5+ft4dAHmAn8bGRHNhJDuwkPm836hFVMSmQfJiTAjXxdifyDDuJz2yNfLw9ABKHpmlITk5GfHw87Ha7t4dD1WAn8bGRHNhJDuwkPhEauRYb3JF5Are+vwnlmg4fm+L2tl3LAADA4ZPF8LPb0DG0GYZ2b4PEuAivjLmpidCJasdO4jNjI04KEBEREZHUUnNPAQBUHdiWebLKc3Yecr+L4JeDJ/DFtsOYN6qPZSYGiIiqwscHiIiIiEhqm9PyYVPq/nE2BdicltfwAyIikggnBYiIiIhIagmxodD0P/5iq5z39twJg3P/8qvpQL+Y0MYfIBGRwBRd13VvD8LsCgsLERwcjIKCAgQFBXl7ONXSdR2apsFms0FR6jHdTk2CncTHRnJgJzmwk/hEabQqJQeb0/Lg8LWjpEyt9Nb1zf/mtDykHClAUlo+4toEYvmjV3ttzE1JlE5UM3YSn0yNPP0+lGsKkBun0wmHw+HtYVAt2El8bCQHdpIDO4lPhEaJcREerQ2QGBeB9OOnMXDGWuzPOYWCM2UIbubbBCP0PhE6Ue3YSXxma8RJATJomoZ9+/aZaiVNM2In8bGRHNhJDuwkPhkbRYc1x8URLbA/5xQeXrIN8e1botipIsDP7tHbhNiKuw6SUvOQEBsqxUKFMnayInYSnxkbcVKAiIiIiCync+tA7M85hfUHjmP9geN1+tiFm9IBVKxVsHBTOncwICKpcaFBIiIiIrIcf58L/2uwpnMHAyKSH+8UIDdmuQXG7NhJfGwkB3aSAzuJT8ZGf45vgy+3H4YCoC6rbisKcO4y3ZoO7DpcgKe/3InScg3+PjbjrY/dhqs6hwtzF4GMnayIncRntkbcfaAJyLL7ABEREZGV1LZjQU07GZxxluOpL3aiuEyr9fPw8QIi8gbuPkB1pus6ioqKEBgYKPz2GlbGTuJjIzmwkxzYSXwyN/J0x4LqrN2Xi6+2H6nxHNfjBd6eFJC5k5Wwk/jM2IhrCpBB0zSkpaVB02qf8SbvYSfxsZEc2EkO7CQ+Kze6Pr4tgD/+Qq2c9xaoeLygX0xoUw6rSlbuJBN2Ep8ZG/FOASIiIiKiekiMi8C8UX2qfAThQE4R1h84jrg2gV6/S4CIqCacFCAiIiIiqqfqHkHYc7QQ69/dgIN5Z1CuavCx8wZdIhIT/+tEbhwOh7eHQB5gJ/GxkRzYSQ7sJD42quziiEAE+vvgtFPF3uwibw8HADvJgp3EZ7ZG3H2gCXD3ASIiIiLruWfhz1i3/xhevDEOo/tHe3s4RGQxnn4fyjsFyKBpGvLy8ky1aIYZsZP42EgO7CQHdhIfG1WvT8dWAIBfDp7w8kjYSRbsJD4zNuKkABl0XUdWVhZ484jY2El8bCQHdpIDO4mPjarXu1PFpMCPe3Oxcne2V8fCTnJgJ/GZsREnBYiIiIiIGkH+aScA4IxTxf0f/opVKTleHhERUWWcFCAiIiIiagTbDp40/t2mAJvT8rw3GCKianBSgNwEBgZ6ewjkAXYSHxvJgZ3kwE7iY6OqJcSGGv+u6UC/mNAazm587CQHdhKf2Rpx94EmwN0HiIiIiKzpmhlrkXH8NCYMvAiTh3Tx9nCIyEK4+wDVmaZpyM7ONtVKmmbETuJjIzmwkxzYSXxsVLPIIH8AQJdI7/5kkZ3kwE7iM2MjTgqQQdd1ZGdnm2olTTNiJ/GxkRzYSQ7sJD42qpnD1w4AKClTvToOdpIDO4nPjI04KUBERERE1EgCBJkUICKqjnSTAhkZGbj33nsRHR2NgIAAxMbG4oUXXoDT6XQ7LzMzEzfeeCOaN2+OsLAwPPLII5XOSU5OxoABAxAQEIB27drhpZdeqjTjs27dOvTu3RsOhwMxMTGYM2dOo18jEREREZmD606BYk4KEJGgfLw9gLrau3cvNE3D3LlzcdFFF2HXrl0YO3YsTp8+jRkzZgAAVFXFsGHDEB4ejo0bNyIvLw/33HMPdF3HrFmzAFQsupCYmIiBAwdi69at2L9/P0aPHo3mzZtj0qRJAID09HRcf/31GDt2LD766CNs2rQJ48ePR3h4OEaMGOG134PGoigKQkJCoCiKt4dCNWAn8bGRHNhJDuwkPjaq2R+PD3j3+WN2kgM7ic+MjUyx+8Abb7yB2bNnIy0tDQDwv//9DzfccAOysrLQtm1bAMAnn3yC0aNHIzc3F0FBQZg9ezamTJmCnJwc+PtXLADz2muvYdasWTh06BAURcGTTz6Jb7/9Fnv27DE+17hx4/Dbb78hKSnJ4/Fx9wEiIiIia3ppaQoWbkrHg9fE4smhXb09HCKyEE+/D5XuToGqFBQUICQkxPh1UlISunfvbkwIAMCQIUNQWlqKX3/9FQMHDkRSUhIGDBhgTAi4zpkyZQoyMjIQHR2NpKQkDB482O1zDRkyBAsWLEBZWRl8fX2rHE9paSlKS0uNXxcWFgKouINBVStuHVMUBTabDZqmuT2y4DruOq+24zabDYqiVHkcQKVVMas7brfboaoqsrKy0K5dO+M8u91eaYzVHRfxmnRdr/K4zNfETuJfk6IoxqSk6xzZr4md5LgmdpLjmszWSdM0HD16FO3atcP5ZL0m1xgbopPDt+L1i53llf4eyE7idBLlmthJ/GvSNA2HDx9Ghw4doCiK0Nfk6c//pZ8USE1NxaxZs/Dmm28ax7KzsxEREeF2XqtWreDn54fs7GzjnE6dOrmd4/qY7OxsREdHV/k6ERERKC8vx/Hjx9GmTZsqxzR9+nRMnTq10vHdu3ejRYsWAICQkBB06NABhw4dQn5+vnFOZGQkIiMjkZGRgaKiIuN4VFQUQkNDceDAAZSUlBjHY2JiEBQUhJSUFLc/BF26dIGfnx+Sk5PdxhAfHw+n04l9+/YZx+x2O+Lj41FUVITU1FScOHGi4n9iDge6du2KEydOICsryzg/MDAQsbGxyM3NNX4/Rb4m1x0kAExzTewk9jXFxcUhJycH+fn5xq1lsl8TO8lxTewkxzWZrZOiKNB1HUFBQcjIyDDFNTVkJ9dCg4ezjyE5udxr18ROclwTO4l/Tbqu48SJE4iKisKpU6eEvqbqvl89nzCPD7z44otVfiN9rq1bt6JPnz7Gr48cOYIBAwZgwIABmD9/vnH8/vvvx8GDB/H999+7fbyfnx8WL16MO+64A4MHD0Z0dDTmzp1rvP/w4cNo3749kpKS0K9fP1x88cUYM2YMpkyZYpyzadMmXHnllTh69CgiIyOrHGdVdwpERUUhPz/fuG1DlJkuF7vdjvLyciQnJ6Nbt26w2+3GcVFmuqw8I3nuGNlJ/GsCgJ07d7o1kv2a2EmOa2InOa7JbJ1UVUVKSgq6d+9uTNzIfk2uMTZEpwUbMzBt+R7c3KMN3rq9h9euiZ3kuCZ2Ev+aVFXF7t27cckllxjjEfWaTp06hZYtW8rz+MCECRNwxx131HjOuT/ZP3LkCAYOHIiEhAT861//cjsvMjISW7ZscTt24sQJlJWVGT/5j4yMdJulAYDc3FwAqPUcHx8fhIaGVjtOf39/t8cSXOx2u9tfaIA//pBVdW5TH1cUBYqiVBpndWOs63FvXVNVx2W/JnYS+5pUVa2yUU1jFP2aAHaqz3F2YqfqjputE8Brqu64w6/i40vL9UqvJes11XSc18Rrqu64ma7JNWEj+jWdP7FUHWEmBcLCwhAWFubRuYcPH8bAgQPRu3dv/Pvf/670m5iQkIBp06bh6NGjxi0TK1euhL+/P3r37m2c8/TTT8PpdMLPz884p23btsbkQ0JCApYuXer22itXrkSfPn2qXU9AZoqiIDIy0uM/POQd7CQ+NpIDO8mBncTHRjULEGRLQnaSAzuJz4yNqp6SENiRI0dwzTXXICoqCjNmzMCxY8eQnZ3t9hP9wYMHIy4uDnfffTe2b9+O1atXY/LkyRg7dqxx28Sdd94Jf39/jB49Grt27cJXX32FV199FRMnTjQCjxs3DgcPHsTEiROxZ88eLFy4EAsWLMDkyZO9cu2NzWazITIystqZKhIDO4mPjeTATnJgJ/GxUc1cCw2WeHlSgJ3kwE7iM2Mj6a5k5cqV+P333/Hjjz+iffv2aNOmjfGPi91ux3fffQeHw4H+/fvj9ttvx/DhwzFjxgzjnODgYKxatQqHDh1Cnz59MH78eEycOBETJ040zomOjsby5cuxdu1a9OzZEy+//DJmzpyJESNGNOk1NxVVVZGamlrpWRQSCzuJj43kwE5yYCfxsVHNXHcKeHtSgJ3kwE7iM2MjYR4f8NTo0aMxevToWs/r0KEDli1bVuM58fHxWL9+fY3nDBgwANu2bavLEKV27uqWJC52Eh8byYGd5MBO4mOj6jkEeXwAYCdZsJP4zNZIujsFiIiIiIhk4TDuFKi8swYRkQg4KUBERERE1EhcawqIcKcAEVFVOClABkVREBUVZaqVNM2IncTHRnJgJzmwk/jYqGairCnATnJgJ/GZsZF0awpQ47HZbAgNDfX2MKgW7CQ+NpIDO8mBncTHRjUL8BNjUoCd5MBO4jNjI94pQAZVVbF3715TraRpRuwkPjaSAzvJgZ3Ex0Y1c/hUTAqUqTrKVe+tK8BOcmAn8ZmxEScFyE1JSYm3h0AeYCfxsZEc2EkO7CQ+Nqqe604BACgp9+5ig+wkB3YSn9kacVKAiIiIiKiR+Pv88dftYqd5frJIRObBSQEiIiIiokaiKIqxA4G31xUgIqoKJwXIYLPZEBMTA5uNfyxExk7iYyM5sJMc2El8bFQ7EXYgYCc5sJP4zNiIuw+QQVEUBAUFeXsYVAt2Eh8byYGd5MBO4mOj2jl87QDKUOzFSQF2kgM7ic+MjcwzvUEXTFVVJCcnm2olTTNiJ/GxkRzYSQ7sJD42qt0fdwp4d/cBdhIfO4nPjI04KUBuzPSH28zYSXxsJAd2kgM7iY+NauZ/dlLAm3cKAOwkC3YSn9kacVKAiIiIiKgRBXChQSISGCcFiIiIiIgaUYCf9xcaJCKqDicFyGCz2dClSxdTraRpRuwkPjaSAzvJgZ3Ex0a1c/icfXzA6d3dB9hJfOwkPjM2Ms+VUIPw8/Pz9hDIA+wkPjaSAzvJgZ3Ex0Y1cwhypwA7yYGdxGe2RpwUIIOmaUhOToameW9lXKodO4mPjeTATnJgJ/GxUe2MOwW8uPsAO8mBncRnxkacFCAiIiIiakQBflxokIjExUkBIiIiIqL/b+/Oo6OsDv+PfyZ7SkIkAbIYCJtUNlHBJXxBKAiKYsGtigtajpyfPYJS7bfuRawttj11K4h1qbh8W1ARC65ExYAgKKthNZIAARMCYUkCZJu5vz8wIyOEBAwz93nm/TqHEzIzefIk795j5/I8955C8dF23D4AAMfCpAAAAABwCsVF198+wKQAAPswKQC/iIgI9erVy1UraboRnexHI2egkzPQyX40alycBVcK0MkZ6GQ/NzZyz0+CZlFTUxPqU0AT0Ml+NHIGOjkDnexHo+P74UqB0C5MRidnoJP93NaISQH4+Xw+bdq0yVUraboRnexHI2egkzPQyX40apwNawrQyRnoZD83NmJSAAAAADiF2H0AgM2YFAAAAABOobio728fqGFSAIB9mBRAgMjIyFCfApqATvajkTPQyRnoZD8aHV9czPe3D9SFdlKATs5AJ/u5rZHHGGNCfRJuV15erqSkJO3fv18tW7YM9ekAAAAgiL7YXKbRLyxV5zYt9Mk9g0J9OgDCRFPfh3KlAPyMMSovLxfzRHajk/1o5Ax0cgY62Y9GjYuvv1IghLsP0MkZ6GQ/NzZiUgB+Pp9PBQUFrlpJ043oZD8aOQOdnIFO9qNR4+KiQ7/QIJ2cgU72c2MjJgUAAACAU6h+S8JD7D4AwEJMCgAAAACnUP2kQFWt11WXHANwByYFECAuLi7Up4AmoJP9aOQMdHIGOtmPRscX+/2kgM9INd7QXXJMJ2egk/3c1ojdB4KA3QcAAADCV02dT10f+kCStOYPw5T0s+gQnxGAcMDuAzhhPp9PZWVlrlo0w43oZD8aOQOdnIFO9qNR46IjPYrwHP57VV1o1hWgkzPQyX5ubMSkAPyMMSoqKuJeN8vRyX40cgY6OQOd7Eejxnk8nh8WG6wJzaQAnZyBTvZzYyMmBQAAAIBTLD7m+8UGQ3SlAAA0hEkBAAAA4BSLjQrtlQIA0JCoUJ8A7JKYmBjqU0AT0Ml+NHIGOjkDnexHo8b5vr/UeMaSLZq3pljZnVMkSV9sLlN8TKQO1XhP+uPxjpXdOUVDu6dKopNT0Ml+bmvE7gNBwO4DAAAA4Stn/U6Ne3V50L+vR5KR9MKYvv6JAQDhg90HcMJ8Pp9KSkpctZKmG9HJfjRyBjo5A53sR6PGfbG5LCTf10iK9Hi0tKCMTg5BJ/u5sRGTAvAzxqikpMRVK2m6EZ3sRyNnoJMz0Ml+NGpc/eX99TzNeOzGjuU1Rhd2SqGTQ9DJfm5sxJoCAAAAwCk0tHuqXhjTV0sLyhQXHamqWq8u7HR4ouDIx07247GO9cmGndpSdlAje2doaPdUeb0scAjg2JgUAAAAAE6xod1Tj3lff3Pe63/ksbqmJuje2XnaUnag2Y4PwJ24fQB+Ho9HycnJ8nia86I2NDc62Y9GzkAnZ6CT/Whkp1+c2VaStGb7fu0sr6KTQ9DJfm5sxO4DQcDuAwAAAAi2UdMWa3XRPv1P5xSd3b7VT97e8Hhfw+4GgH2a+j6USYEgcMqkgM/n0/bt25WZmamICC4isRWd7EcjZ6CTM9DJfjSy129nrdacVTuC8r36ZrVSh5SfSR6PLumRxiTBSWI82c9JjdiSECfMGKM9e/a4aiVNN6KT/WjkDHRyBjrZj0b2CubVzcu37tVbK3forRXbNe7V5cpZvzN439xFGE/2c2MjJgUAAAAAFxreM/2EXt9ccwgeHd4JAYAzsPsAAAAA4EJHboUYG+nR9pKdykxLVbXXnND2hsf7uKmkXB9vKFWEJN/339dI/uMAsB+TAvDzeDxKS0tz1UqabkQn+9HIGejkDHSyH43sVr8Vos/nU2lpK7Vt27ZJ90GfyJoAOet3amlBmfYdrNHslTuUFB/FmgInifFkPzc2YqHBIHDKQoMAAADAyTpQXaeej3wkY6TlD12s1gmxoT4lIKyx0CBOmNfr1ebNm+X1ekN9KjgOOtmPRs5AJ2egk/1o5AzB6NQiNkpZyT+TJG0oLj9l38fNGE/2c2MjJgUQoKKiItSngCagk/1o5Ax0cgY62Y9GzhCMTt3SD/9rJJMCJ4/xZD+3NWJSAAAAAECz+GFSwF1vmgA3Y1IAAAAAQLPgSgHAeZgUgJ/H41G7du1ctZKmG9HJfjRyBjo5A53sRyNnCFanbumJkqRvSytVXeeee66DhfFkPzc2YktC+EVERCglhT1lbUcn+9HIGejkDHSyH42cIVidTj8tXvHRETpU69PrS7eqfXILfbG5TPExkTpU4z3pj9mdD5/7TznWsY6R3TnFqu0TGU/2c2MjtiQMAqdsSej1epWfn68zzjhDkZGRoT4dNIBO9qORM9DJGehkPxo5Q7A65azfqXGvLj9lx29OHo9kjPTCmL7WTAwwnuznpEZsSYiTUlVVFepTQBPQyX40cgY6OQOd7EcjZwhGpy82l8kpF1UbI0V4pKUFZaE+lQCMJ/u5rRG3DwAAAABoFtmdU/SvxYWK8Ei+Zroe2SPppx6qoWP4jLTom10a869lqvUaRUd6mvSxY+sWkqTC3Qf8j8VEetQyPlrtWv1MVbU+625NABrCpAAAAACAZjG0e6peGNNXSwvKdGGnw/ddLy0oU1x0pKpqvSf9sTmOdeQxoiI8mvnVNu0/VKdvSiv1TWnlCf2cX2xu/OqCfy0uVM+MlspoFa/46EiNOCuDSQJYiTUFgsApawoYY1RRUaHExERXrabpNnSyH42cgU7OQCf70cgZ6HS0h+bk6f+WbfvJVyGciME/b6tuGS0bXOSQTvZzUqOmvg9lUiAInDIpAAAAAISL+kURIyT59MMtBo19PPLWiIa+tqlsWuQQ7tPU96HcPgA/r9er9evXq3v37tavpBnO6GQ/GjkDnZyBTvajkTPQ6WhH3urQ3LczbCop18cbSv2TBg35YvPugEkBOtnPjY2YFEAAr9cb6lNAE9DJfjRyBjo5A53sRyNnoNPRhnZP/Un/Un+8r81Zv9M/aVA/SfDjKwkW5e9Wzvqdkg6vUxAX5VFRcZnabf9GVXVG8TGROlTjbfLH7M4p/mOd6NeeimMd6xhuWIDRbWOJSQEAAAAAaGY/nnA4cpLgkw07tbGkQvmllRr36vKjv/ibgpP6nv9aXHiyp3tKjvXjY3i+f4zbJuzCpAAAAAAAnGJHThIcqqnTxpKKEJ9R8NVfJfFEziZ9VbhHh2q9io+OPOpjrc+nfp1bM3EQJCw0GAROWWjQGKOqqirFxcVZv5JmOKOT/WjkDHRyBjrZj0bOQCe71C9y6PFIzfVu7EQXOTzVx2qOY9h4RYGTxhILDeKkxMTEhPoU0AR0sh+NnIFOzkAn+9HIGehkjyMXOTxy0cLYqAhV1dYpLjpK1XW+Zl8AMdjH+vEx3lm1XTv2VTX597Qof5d1kwKS+8YSVwoEgVOuFPB6vcrLy1OvXr1cs5KmG9HJfjRyBjo5A53sRyNnoJMzuL1TU7eBrNc2MVYXdEpWTZ1P3dIPv4/aUFyumKgI1dT5TvrjiR4rNipSl/RI09DuqY5qxJUCAAAAAABrNHUbyGUFZVq+da9KK6o1b02xJOmjdTub7TxO5lhvrdiuF8b01eCft26287AFkwIAAAAAgKBoyjaQj85br5Xb9spn0TXtHs/h2yDcOCkQEeoTAAAAAACgXnbnFPnMD29WI45Yz6/+Mc9JfjyZY0mHF4SsXyPBbVhTIAicsqaAMUY+n08RERHWr6QZzuhkPxo5A52cgU72o5Ez0MkZ6PSDnPU7A24zCNUiij7j08uLt0qSFt83WBlJcY5pxJoCOCk1NTWKi4sL9WmgEXSyH42cgU7OQCf70cgZ6OQMdDqsodsMmnM3gqYea913FfqycI8+XFuisf/TwXWNuH0Afj6fT5s2bZLP5wv1qeA46GQ/GjkDnZyBTvajkTPQyRnoZKfLeqZJkv6Zu1l/+3Cjfv+fZZq/riTEZ9V8mBQAAAAAAKABP4s9fIF9aUW1ns0t0LxvDuj/vb5SOeubb0eEUGJSAAAAAACABmwsrjjqsfrdCNyASQEEiIyMDPUpoAnoZD8aOQOdnIFO9qORM9DJGehkn+zOhxcmPHJXAjftRsDuA0HglN0HAAAAAABHO9ZuCM256OGpwO4DOGHGGFVUVCgxMdH67TXCGZ3sRyNnoJMz0Ml+NHIGOjkDnexVvxvCkY3cgtsH4Ofz+VRQUMBqp5ajk/1o5Ax0cgY62Y9GzkAnZ6CT/dzYiEkBAAAAAADCFJMCAAAAAACEKSYFECAuLi7Up4AmoJP9aOQMdHIGOtmPRs5AJ2egk/3c1ojdB4KA3QcAAAAAAMHU1PehXCkAP5/Pp7KyMlctmuFGdLIfjZyBTs5AJ/vRyBno5Ax0sp8bGzEpAD9jjIqKisTFI3ajk/1o5Ax0cgY62Y9GzkAnZ6CT/dzYiEkBAAAAAADCFJMCAAAAAACEKSYFECAxMTHUp4AmoJP9aOQMdHIGOtmPRs5AJ2egk/3c1ojdB4KA3QcAAAAAAMHE7gM4YT6fTyUlJa5aSdON6GQ/GjkDnZyBTvajkTPQyRnoZD83NmJSAH7GGJWUlLhqJU03opP9aOQMdHIGOtmPRs5AJ2egk/3c2IhJAQAAAAAAwhSTAgAAAAAAhCkmBeDn8XiUnJwsj8cT6lPBcdDJfjRyBjo5A53sRyNnoJMz0Ml+bmzE7gNBwO4DAAAAAIBgYvcBnDCfz6dt27a5aiVNN6KT/WjkDHRyBjrZj0bOQCdnoJP93NiISQH4GWO0Z88eV62k6UZ0sh+NnIFOzkAn+9HIGejkDHSynxsbMSkAAAAAAECYigr1CYSD+lmk8vLyEJ/J8Xm9XlVWVqq8vFyRkZGhPh00gE72o5Ez0MkZ6GQ/GjkDnZyBTvZzUqP695+NXdXApEAQVFRUSJLatWsX4jMBAAAAAISTiooKJSUlNfg8uw8Egc/n03fffafExESrt64oLy9Xu3btVFRUxC4JFqOT/WjkDHRyBjrZj0bOQCdnoJP9nNTIGKOKigplZGQoIqLhlQO4UiAIIiIilJmZGerTaLKWLVta/z9w0MkJaOQMdHIGOtmPRs5AJ2egk/2c0uh4VwjUY6FBAAAAAADCFJMCAAAAAACEKSYF4BcbG6tJkyYpNjY21KeC46CT/WjkDHRyBjrZj0bOQCdnoJP93NiIhQYBAAAAAAhTXCkAAAAAAECYYlIAAAAAAIAwxaQAAAAAAABhikkBAAAAAADCFJMCkCQ9++yz6tixo+Li4tSnTx8tWrQo1KcU1h555BF5PJ6AP2lpaf7njTF65JFHlJGRofj4eA0aNEjr1q0L4RmHh4ULF+qKK65QRkaGPB6P3nnnnYDnm9KlurpaEyZMUOvWrdWiRQv98pe/1Pbt24P4U7hbY41uvfXWo8bWhRdeGPAaGp1aU6ZM0XnnnafExES1bdtWo0aN0qZNmwJew1gKvaZ0YjyF3vTp03XWWWepZcuWatmypbKzs/XBBx/4n2cshV5jjRhHdpoyZYo8Ho8mTpzof8zN44lJAWjWrFmaOHGiHnzwQa1atUoDBgzQ8OHDtW3btlCfWljr0aOHiouL/X/y8vL8z/31r3/VE088oalTp+qrr75SWlqahg4dqoqKihCesfsdOHBAvXv31tSpU4/5fFO6TJw4UXPmzNHMmTP1+eefq7KyUiNGjJDX6w3Wj+FqjTWSpEsvvTRgbL3//vsBz9Po1MrNzdUdd9yhpUuXKicnR3V1dRo2bJgOHDjgfw1jKfSa0kliPIVaZmamHn/8cS1fvlzLly/X4MGDNXLkSP8bFcZS6DXWSGIc2earr77S888/r7POOivgcVePJ4Owd/7555vbb7894LEzzzzT3HfffSE6I0yaNMn07t37mM/5fD6TlpZmHn/8cf9jVVVVJikpyTz33HNBOkNIMnPmzPF/3pQu+/btM9HR0WbmzJn+1+zYscNERESYDz/8MGjnHi5+3MgYY2655RYzcuTIBr+GRsFXWlpqJJnc3FxjDGPJVj/uZAzjyVatWrUyL774ImPJYvWNjGEc2aaiosKcccYZJicnxwwcONDcddddxhj3/7eJKwXCXE1NjVasWKFhw4YFPD5s2DAtWbIkRGcFScrPz1dGRoY6duyo66+/XgUFBZKkwsJClZSUBDSLjY3VwIEDaRZCTemyYsUK1dbWBrwmIyNDPXv2pF0QffbZZ2rbtq26du2qcePGqbS01P8cjYJv//79kqTk5GRJjCVb/bhTPcaTPbxer2bOnKkDBw4oOzubsWShHzeqxziyxx133KHLL79cF198ccDjbh9PUaE+AYTW7t275fV6lZqaGvB4amqqSkpKQnRWuOCCC/Tqq6+qa9eu2rlzpx577DH169dP69at83c5VrOtW7eG4nQhNalLSUmJYmJi1KpVq6New3gLjuHDh+vaa69VVlaWCgsL9fDDD2vw4MFasWKFYmNjaRRkxhjdfffd6t+/v3r27CmJsWSjY3WSGE+2yMvLU3Z2tqqqqpSQkKA5c+aoe/fu/jchjKXQa6iRxDiyycyZM7Vy5Up99dVXRz3n9v82MSkASZLH4wn43Bhz1GMInuHDh/v/3qtXL2VnZ6tz58565ZVX/IvP0MxOJ9OFdsFz3XXX+f/es2dP9e3bV1lZWXrvvfd01VVXNfh1NDo1xo8fr6+//lqff/75Uc8xluzRUCfGkx1+/vOfa/Xq1dq3b59mz56tW265Rbm5uf7nGUuh11Cj7t27M44sUVRUpLvuukvz589XXFxcg69z63ji9oEw17p1a0VGRh41e1VaWnrUTBhCp0WLFurVq5fy8/P9uxDQzC5N6ZKWlqaamhrt3bu3wdcguNLT05WVlaX8/HxJNAqmCRMmaO7cuVqwYIEyMzP9jzOW7NJQp2NhPIVGTEyMunTpor59+2rKlCnq3bu3nn76acaSRRpqdCyMo9BYsWKFSktL1adPH0VFRSkqKkq5ubl65plnFBUV5f9du3U8MSkQ5mJiYtSnTx/l5OQEPJ6Tk6N+/fqF6KzwY9XV1dqwYYPS09PVsWNHpaWlBTSrqalRbm4uzUKoKV369Omj6OjogNcUFxdr7dq1tAuRsrIyFRUVKT09XRKNgsEYo/Hjx+vtt9/Wp59+qo4dOwY8z1iyQ2OdjoXxZAdjjKqrqxlLFqtvdCyMo9AYMmSI8vLytHr1av+fvn376sYbb9Tq1avVqVMnd4+nIC9sCAvNnDnTREdHm5deesmsX7/eTJw40bRo0cJs2bIl1KcWtu655x7z2WefmYKCArN06VIzYsQIk5iY6G/y+OOPm6SkJPP222+bvLw8M3r0aJOenm7Ky8tDfObuVlFRYVatWmVWrVplJJknnnjCrFq1ymzdutUY07Qut99+u8nMzDQff/yxWblypRk8eLDp3bu3qaurC9WP5SrHa1RRUWHuueces2TJElNYWGgWLFhgsrOzzemnn06jIPrNb35jkpKSzGeffWaKi4v9fw4ePOh/DWMp9BrrxHiyw/33328WLlxoCgsLzddff20eeOABExERYebPn2+MYSzZ4HiNGEd2O3L3AWPcPZ6YFIAxxphp06aZrKwsExMTY84999yALYcQfNddd51JT0830dHRJiMjw1x11VVm3bp1/ud9Pp+ZNGmSSUtLM7Gxseaiiy4yeXl5ITzj8LBgwQIj6ag/t9xyizGmaV0OHTpkxo8fb5KTk018fLwZMWKE2bZtWwh+Gnc6XqODBw+aYcOGmTZt2pjo6GjTvn17c8sttxz1+6fRqXWsPpLMyy+/7H8NYyn0GuvEeLLD2LFj/f//rU2bNmbIkCH+CQFjGEs2OF4jxpHdfjwp4Obx5DHGmOBdlwAAAAAAAGzBmgIAAAAAAIQpJgUAAAAAAAhTTAoAAAAAABCmmBQAAAAAACBMMSkAAAAAAECYYlIAAAAAAIAwxaQAAAAIGp/PF+pTAAAAR2BSAAAAHFdVVZV27tx51ONPP/20duzYoU2bNsnj8aisrKzRYz3xxBPq37+///N169b5v7agoECjRo3Sd99916Tzev/999WrVy+Vl5c3+tovv/xSn3zyiSTpnHPO0axZs1RbW6t77rlHu3btkiTdcccdeuONN5r0vQEAcAsmBQAAwHE9+uijGjBggEpKSvyPrVy5UhMnTlR1dXWTj7N792499thjuummm/yPtWrVSpK0d+9edezYUVlZWbrwwgu1efNm/2vmzZunyZMn69ChQwHHmzdvnmpra9WyZctGv3d0dLRuuukmHTx40P/YU089pVWrViklJUWS9Pbbb+u0005r8s8DAIAbRIX6BAAAgN0mT56svLw8XXbZZVqyZIni4uI0a9Yspaenq7a2VgUFBZKk/Px87d69W5Lk8XjUtWvXgONMmjRJbdq00W233eZ/7MhJAY/Ho6effloREREaNmyY1qxZo4SEBLVs2VL//ve/9corr+g///mPLrjgAknSBx98oH79+mnr1q1HnbPX61X79u0VFRWlqqoqJScn6+KLL9ayZctUU1OjXbt2af369frzn/+sXbt2qbq6Wjt37tT5559/Sn6HAADYymOMMaE+CQAAYLfKykr17dtX9957ry677DJ169ZNe/fubfD1cXFxAf+yv3btWp1zzjl66aWXNGbMmKNeO2/ePA0dOlSSZIzRl19+6X/zL0nV1dX67W9/qzfeeEOFhYXatm2bevbsedxzvvnmm/Xqq6/q888/1+jRoyUdvhVi9+7dio+P918hkJ2drV/96le69tprjzrGiBEjNG/evEZ+OwAAOBdXCgAAgEYlJCRo+fLlSkhI0A033KDU1FRt27ZNCQkJ2rhxo7p166bdu3f732gfaf/+/br66qtVV1enrKysgOe8Xq+SkpK0ePFi7dy5U4WFhdqyZYsKCwt10003aezYsZKk2NhYPfvss3rggQeUmJio559/XmeccYbWrFmjqqoqbdu2TV26dFFUVJRqa2uVlZWlgQMHSpL69++voqIi1dbW6vLLL1dOTo48Ho9uu+02PfDAA4qOjtaECRPUq1cvvfnmm5Kk8ePHq0ePHvrd7353in+zAACEFpMCAACgQXv37tWnn37q/3z48OG68847lZKSooSEBEmHryKQ5P/8SIcOHdLVV18dsPbAXXfdpby8PG3ZskVFRUWqq6vT3/72N3Xq1EkdOnRQhw4dNGLECJ1zzjlHHS8zM1PFxcV66aWXNGXKFMXHx2vZsmX6xS9+4Z+UyM3NVWVlpUaNGuX/ugMHDmjs2LGqrq7WmWeeqQkTJmj27NmaN2+e5s2bp5kzZ+rgwYPq0qWLIiMjtWPHDk2YMEGZmZnN9asEAMBKTAoAAIAG7dq1S88995xqamq0cOFCbdmyRcOHD9e+ffuOem1cXFzA57feequKi4u1YcMGffzxx+revbukw2/s27dvr6ysLHXo0EGTJ09WVlaWpk6d2qRzmjhxYsDaBPWLB0ZFHf6/NdOnT9cNN9zgv2ph3bp1uuKKK9SmTRvNnz9fgwYNUkpKij788ENNnTpV7777rtq2bauysjJt3LhRZ5xxhvLz83X22WefzK8MAABHYVIAAAA0qGvXrsrJyVFZWZlat24t6fDOAz6fz/+ae++9VwsWLNA777yjjIwM/+OJiYl6/fXX9fe//12dOnXyP/6///u/Ad/jvPPO828X2JgdO3ZoypQpKikpUXx8vCT5t0Lcvn27kpKS1Lt3b11zzTX+r8nMzNQ111yjP/7xj9qzZ4/Gjh2rgQMHKjo6WnfddZc6dOigSZMmKScnRwsWLFB5ebkSExPVvn37E/xtAQDgPEwKAACAE9KxY0f/3/fv369PP/1UqampGjNmjD766KOAXQfuvvtuSTru1oU9evTQU0891ej33bx5s84++2zl5uaqX79+/seXLVsmSZo7d6569OihRx55JODrkpKS9NBDD6mqqkpPPvmk1q5dqzFjxmj//v2KjIzU888/r0GDBqmurk5vvvmmSkpKdNlllzXlVwEAgOOx+wAAAGhU/ZUCW7ZsCVgscNy4cZo/f742btyo8ePHa+7cufroo4907rnnBnx9dXW14uLi9Nlnn/kXAKz37bff+hcNPOussxo8hyFDhujQoUNavHixPB6P//H27dururpa6enpWr169TG/tlWrVse85SEpKUklJSWKi4tTaWmpsrKy1K5dOz311FNMDAAAwkJEqE8AAAA4jzFG9957r2bMmKHXX39d8fHxevHFF3XNNdfokksu0YYNG5p8rC5duqhbt26aM2eO/7Hy8vKAN/H//Oc/tXDhQk2fPj1gQuC///2vioqK9NZbb6mwsNC/e8CP7d27V48++qgGDx4sY4yMMRo7dqxGjx7tXwuhbdu2GjRokPbu3athw4ad4G8EAABnYlIAAACckPXr16t///76xz/+oTlz5mjAgAGSJI/Ho2nTpumiiy7SyJEj/bsSNMX111+v1157TTU1NZKk1atXa+jQoZKkDRs26O6779bdd9+t3r17+7+mpKREEyZM0OjRozVgwABNmDBB9913n/bs2XPU8ffs2aO//OUvSk5OVlFRkebOnavZs2frvvvu879m3759+vbbb1VdXa1t27ad1O8GAACnYVIAAAAcl8/n0/Tp0yVJ999/v8rLy9WmTRutWbNGI0aMCHhtRESEZsyYoXHjxqlFixZN/h4333yzioqK9Kc//UmStGbNGrVr106S9Otf/1rt27fX5MmT/a/fsWOHLr30UkVHR2vatGmSpIcfflhxcXG69NJLVV5eHnD85ORkrVmzRqmpqerZs6duuukmTZkyxX8rhM/n0+jRo5WWlqYhQ4bohhtuUF1d3Qn+pgAAcB4mBQAAQIN8Pp9uvPFGzZgxQx9++KGSk5N15ZVXqmvXrtq3b5+2b9+u8vJyHThwQHv27NF3332n7du369xzz9Ubb7zR5O/TsWNH/eEPf9Cjjz6qYcOG6cknn9TFF18s6fCtA6+//rr/Mv+5c+eqb9++qq2t1aJFi9SqVStJUmxsrN59912VlZXp7LPP1syZM3Xk0kktWrRQdXW1YmJiNHToUN1///1atGiRiouLNXLkSG3evFnvvPOOXn75ZZWUlOjKK6885joEAAC4CZMCAACgQfn5+crNzdVHH32kSy65RFOnTtV7772nyspKjRo1Sp06dVJSUpISEhKUkpKi008/Xd27d9fw4cM1Y8aMgGMlJCQoMjKywe/14IMP6plnnlFhYaFSU1N18803S5J69+6tPn36SJKmTZumK6+8UldddZWWLFkSsAWidHhyYfHixbrwwgv1+9//Xvv27dOmTZs0dOhQde7cWdXV1Vq7dq1mz56tnJwcHTp0SD169FBlZaU+/fRTpaSk6LTTTtP8+fOVn5+vwYMHB2y/CACA27D7AAAAOK59+/bptNNOa/D5yspK1dTUKCIiQjExMYqNjT3um/+fwuv1auPGjerRo0ejrz1w4IBatGihuro6vfbaaxo5cqSSk5MDXlNcXKxZs2bpzjvvVERE4L+V7N+/Xxs3btQFF1zQrD8DAAA2YVIAAAAAAIAwxe0DAAAAAACEKSYFAAAAAAAIU0wKAAAAAAAQppgUAAAAAAAgTDEpAAAAAABAmGJSAAAAAACAMMWkAAAAAAAAYYpJAQAAAAAAwhSTAgAAAAAAhCkmBQAAAAAACFP/H0dy72jzhoRPAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 1200x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-> 正在为情景 'PSO_GPU_Final_Analysis' 生成地图...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_10725/520654957.py:36: DeprecationWarning: The 'unary_union' attribute is deprecated, use the 'union_all()' method instead.\n",
      "  map_center = [boundary_gdf_wgs84.unary_union.centroid.y, boundary_gdf_wgs84.unary_union.centroid.x]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-> 地图已成功保存至: final_maps/PSO_GPU_Final_Analysis_map.html\n",
      "\n",
      "🎉 [完成] 所有分析和可视化任务已结束。\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import geopandas as gpd\n",
    "import pandas as pd\n",
    "import pickle\n",
    "import matplotlib.pyplot as plt\n",
    "from shapely.geometry import Point\n",
    "import traceback\n",
    "import os\n",
    "import matplotlib.font_manager as fm\n",
    "\n",
    "print(\"\\n--- 开始加载和分析优化结果 ---\")\n",
    "try:\n",
    "    # 1. 从结果文件中加载数据\n",
    "    with open(\"pso_results.pkl\", \"rb\") as f:\n",
    "        results = pickle.load(f)\n",
    "    print(\"✅ 结果文件 'pso_results.pkl' 加载成功。\")\n",
    "\n",
    "    # 2. 从结果字典中解包数据\n",
    "    optimal_locations_3857 = results.get(\"optimal_locations_3857\", [])\n",
    "    analysis_metrics = results.get(\"analysis_metrics\", {})\n",
    "    convergence_history = results.get(\"convergence_history\", [])\n",
    "    stage1_end_iter = results.get(\"stage1_end_iteration\", -1)\n",
    "    \n",
    "    if not optimal_locations_3857:\n",
    "        raise ValueError(\"结果文件中未找到有效的最优位置坐标。\")\n",
    "\n",
    "    # 3. 将米制坐标转换为WGS-84经纬度，便于理解\n",
    "    optimal_gdf_3857 = gpd.GeoDataFrame(geometry=[Point(loc) for loc in optimal_locations_3857], crs=\"EPSG:3857\")\n",
    "    optimal_gdf_wgs84 = optimal_gdf_3857.to_crs(\"EPSG:4326\")\n",
    "    optimal_locations_wgs84 = [(p.x, p.y) for p in optimal_gdf_wgs84.geometry]\n",
    "\n",
    "    # 4. 打印一份清晰、详细的最终方案分析报告\n",
    "    print(\"\\n\" + \"=\"*40 + \" ✨ 最终方案分析报告 ✨ \" + \"=\"*40)\n",
    "    print(f\"\\n--- 优化摘要 ---\")\n",
    "    print(f\"  - 最终找到 {len(optimal_locations_wgs84)} 个最优站点位置。\")\n",
    "    model_score = analysis_metrics.get('model_score', {})\n",
    "    print(f\"  - 模型最终得分 (越低越好): {model_score.get('total', 'N/A'):.4f}\")\n",
    "    \n",
    "    print(\"\\n--- 关键业务指标 ---\")\n",
    "    pop_coverage = analysis_metrics.get('population_coverage', {})\n",
    "    poi_service = analysis_metrics.get('poi_service', {})\n",
    "    spacing_stats = analysis_metrics.get('spacing_stats', {})\n",
    "    print(f\"  - 📈 实际人口覆盖率: {pop_coverage.get('coverage_ratio', 0) * 100:.2f}%\")\n",
    "    print(f\"  - 🎯 POI服务率: {poi_service.get('service_ratio', 0) * 100:.2f}%\")\n",
    "    print(f\"  - 📏 站点平均间距: {spacing_stats.get('average_distance_m', 0):.0f} 米\")\n",
    "    print(f\"  - ⚠️ 站点最小间距: {spacing_stats.get('min_distance_m', 0):.0f} 米\")\n",
    "\n",
    "    print(\"\\n--- 成本构成 (0-1, 越低越好) ---\")\n",
    "    print(f\"  - POI便利性成本 (加权):   {model_score.get('poi_weighted', 'N/A'):.4f}\")\n",
    "    print(f\"  - 人口覆盖成本 (加权):     {model_score.get('population_weighted', 'N/A'):.4f}\")\n",
    "    print(f\"  - 站点间距成本 (加权):     {model_score.get('spacing_weighted', 'N/A'):.4f}\")\n",
    "    \n",
    "    print(\"\\n--- 最优站点坐标 (WGS84 经纬度) ---\")\n",
    "    for i, (lon, lat) in enumerate(optimal_locations_wgs84):\n",
    "        print(f\"  - 站点 {i+1:02d}: (经度 {lon:.6f}, 纬度 {lat:.6f})\")\n",
    "    print(\"=\"*100)\n",
    "    \n",
    "    print(\"\\n正在生成收敛曲线图...\")\n",
    "    if not convergence_history or not all(isinstance(x, (int, float, np.float64)) for x in convergence_history):\n",
    "        print(\"⚠️ 未能生成收敛曲线图，因为历史分数数据为空或格式不正确。\")\n",
    "    else:\n",
    "        # --- 设置中文字体 (硬编码路径法) ---\n",
    "        font_path = 'SourceHanSansSC-Regular.otf' # 确保这个字体文件在您的项目目录下\n",
    "        if os.path.exists(font_path):\n",
    "            my_font = fm.FontProperties(fname=font_path)\n",
    "            print(f\"✅ 成功加载本地中文字体: {font_path}\")\n",
    "        else:\n",
    "            my_font = fm.FontProperties()\n",
    "            print(f\"❌ 警告: 未在'{font_path}'找到字体文件，中文可能无法显示。\")\n",
    "            # 备用方案：全局设置\n",
    "            plt.rcParams['font.sans-serif'] = ['WenQuanYi Micro Hei', 'SimHei']\n",
    "            plt.rcParams['axes.unicode_minus'] = False\n",
    "\n",
    "        plt.figure(figsize=(12, 7))\n",
    "        plt.plot(convergence_history, marker='.', linestyle='-', markersize=4, label='收敛过程')\n",
    "        \n",
    "        # 使用 fontproperties 参数来应用字体\n",
    "        plt.title(\"PSO 优化收敛曲线\", fontproperties=my_font, fontsize=16)\n",
    "        plt.xlabel(\"迭代次数\", fontproperties=my_font, fontsize=12)\n",
    "        plt.ylabel(\"目标函数值 (成本)\", fontproperties=my_font, fontsize=12)\n",
    "        \n",
    "        legend = plt.legend(prop=my_font)\n",
    "        \n",
    "        plt.grid(True, linestyle='--', alpha=0.6)\n",
    "        plt.show()\n",
    "\n",
    "    try:\n",
    "        ruixing_df = pd.read_csv(ruixing_csv, encoding='utf-8')\n",
    "    except UnicodeDecodeError:\n",
    "        ruixing_df = pd.read_csv(ruixing_csv, encoding='gbk')\n",
    "\n",
    "    # 2. 直接使用原始经纬度列创建 GeoDataFrame\n",
    "    #    (确保CSV中的列名是 'geolocation_lng' 和 'geolocation_lat')\n",
    "    ruixing_gdf_wgs84 = gpd.GeoDataFrame(\n",
    "        ruixing_df, \n",
    "        geometry=gpd.points_from_xy(ruixing_df.geolocation_lng, ruixing_df.geolocation_lat),\n",
    "        crs=\"EPSG:4326\"  # <-- 关键：我们明确声明这些坐标是WGS-84标准\n",
    "    )      \n",
    "   \n",
    "    create_scenario_map(\n",
    "        scenario_name=\"PSO_GPU_Final_Analysis\",\n",
    "        optimal_locations_3857=optimal_locations_3857,\n",
    "        boundary_gdf_wgs84=boundary_gdf_wgs84,\n",
    "        poi_gdf_wgs84=poi_gdf_wgs84,\n",
    "        population_gdf_3857=population_gdf_3857,\n",
    "        road_network_gdf_3857=road_gdf,\n",
    "        actual_locations_gdf=ruixing_gdf_wgs84\n",
    "    )\n",
    "    print(\"\\n🎉 [完成] 所有分析和可视化任务已结束。\")\n",
    "\n",
    "except FileNotFoundError:\n",
    "    print(f\"\\n❌ 错误：找不到结果文件 'pso_results.pkl'。\")\n",
    "    print(\"   请确认您已经成功运行了【单元格 3】并且没有发生错误。\")\n",
    "except Exception as e:\n",
    "    print(f\"\\n❌ [严重错误] 在加载或分析结果时发生错误: {e}\")\n",
    "    traceback.print_exc()"
   ]
  }
 ],
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